init

basic form of csp

arguments/__init__.py

@@ -89,8 +89,10 @@ class OptimizationParams(ParamGroup):
         self.percent_dense = 0.01
         self.lambda_dssim = 0.2
         self.densification_interval = 100
+        # self.densification_interval = 10
         self.opacity_reset_interval = 3000
         self.densify_from_iter = 500
+        # self.densify_from_iter = 10
         self.densify_until_iter = 15_000
         self.densify_grad_threshold = 0.0002
         self.depth_l1_weight_init = 1.0

environment.yml

@@ -1,4 +1,4 @@
-name: gaussian_splatting
+name: 3dgs_v2
 channels:
   - pytorch
   - conda-forge
@@ -6,7 +6,7 @@ channels:
 dependencies:
   - cudatoolkit=11.6
   - plyfile
-  - python=3.7.13
+  - python=3.8
   - pip=22.3.1
   - pytorch=1.12.1
   - torchaudio=0.12.1

projection_tensor.py

@@ -0,0 +1,861 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import os
+import torch
+from random import randint
+from utils.loss_utils import l1_loss, ssim
+from gaussian_renderer import render, network_gui
+import sys
+from scene import Scene, GaussianModel
+from utils.general_utils import safe_state
+import uuid
+from tqdm import tqdm
+from utils.image_utils import psnr
+from argparse import ArgumentParser, Namespace
+from arguments import ModelParams, PipelineParams, OptimizationParams
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+# import open3d as o3d
+import numpy as np
+import cv2
+from collections import Counter
+import pandas as pd
+import ffmpeg
+
+
+def save_ply(xyz_tensor, output_file_path):
+    """
+    xyz_tensor: torch.Tensor of shape [N, 3]
+    output_file_path: string, path to save the PLY file
+    """
+    # 출력 디렉토리가 존재하지 않으면 생성합니다.
+    output_dir = os.path.dirname(output_file_path)
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 텐서를 CPU로 이동하고 NumPy 배열로 변환합니다.
+    xyz_np = xyz_tensor.detach().cpu().numpy()
+
+    # PLY 파일의 헤더를 작성합니다.
+    num_vertices = xyz_np.shape[0]
+    ply_header = f'''ply
+format ascii 1.0
+element vertex {num_vertices}
+property float x
+property float y
+property float z
+end_header
+'''
+
+    # PLY 파일에 데이터를 저장합니다.
+    with open(output_file_path, 'w') as f:
+        f.write(ply_header)
+        np.savetxt(f, xyz_np, fmt='%f %f %f')
+
+    print(f"PLY 파일이 {output_file_path} 경로에 저장되었습니다.")
+
+def pad_image_to_even_dimensions(image):
+    # image는 (height, width, channels)의 NumPy 배열입니다.
+    height, width = image.shape[:2]
+    new_height = height if height % 2 == 0 else height + 1
+    new_width = width if width % 2 == 0 else width + 1
+
+    # 새로운 크기의 배열을 생성하고, 패딩된 영역은 0으로 채웁니다.
+    padded_image = np.zeros((new_height, new_width, *image.shape[2:]), dtype=image.dtype)
+    padded_image[:height, :width, ...] = image
+
+    return padded_image
+
+def compression_h265(projected_data, output_directory):
+    # 출력 디렉토리가 존재하지 않으면 생성합니다.
+    os.makedirs(output_directory, exist_ok=True)
+    
+    # 압축할 텐서 목록
+    tensors_to_process = {
+        'feature_dc_tensor': projected_data.get('feature_dc_tensor'),
+        'scaling_tensor': projected_data.get('scaling_tensor'),
+        'opacity_tensor': projected_data.get('opacity_tensor'),
+        'rotation_tensor': projected_data.get('rotation_tensor'),
+    }
+    
+    # 지정된 텐서들을 처리합니다.
+    for tensor_name, tensor in tensors_to_process.items():
+        if tensor is None:
+            print(f"{tensor_name}가 projected_data에 없습니다.")
+            continue
+        
+        # 텐서를 CPU로 이동하고 NumPy 배열로 변환합니다.
+        tensor_np = tensor.detach().cpu().numpy()
+        
+        # 데이터 타입이 uint8인지 확인하고 변환합니다.
+        if tensor_np.dtype != np.uint8:
+            tensor_np = tensor_np.astype(np.uint8)
+        
+        # 텐서의 차원을 확인합니다.
+        if tensor_np.ndim != 4:
+            print(f"{tensor_name} 텐서의 차원이 4가 아닙니다. 건너뜁니다.")
+            continue
+        
+        num_frames, height, width, channels = tensor_np.shape
+        print(f"{tensor_name} 크기: {tensor_np.shape}")
+        
+        # 채널 수에 따른 픽셀 포맷 설정
+        if channels == 1:
+            pix_fmt = 'gray'
+            tensor_np = tensor_np.squeeze(-1)  # 채널 차원 제거
+        elif channels == 3:
+            pix_fmt = 'rgb24'
+        elif channels == 4:
+            pix_fmt = 'rgba'
+        else:
+            print(f"{tensor_name}의 채널 수 {channels}는 지원되지 않습니다. 건너뜁니다.")
+            continue
+        
+        # 모든 프레임에 대해 패딩을 적용합니다.
+        padded_frames = []
+        for frame in tensor_np:
+            padded_frame = pad_image_to_even_dimensions(frame)
+            padded_frames.append(padded_frame)
+        
+        # 패딩된 프레임의 크기를 가져옵니다.
+        padded_height, padded_width = padded_frames[0].shape[:2]
+        print(f"{tensor_name} 패딩 후 크기: {(padded_height, padded_width)}")
+        
+        # 출력 파일 경로 설정
+        output_video_path = os.path.join(output_directory, f"{tensor_name}.mp4")
+        
+        # ffmpeg 프로세스 설정
+        process = (
+            ffmpeg
+            .input('pipe:', format='rawvideo', pix_fmt=pix_fmt, s=f'{padded_width}x{padded_height}')
+            .output(output_video_path, vcodec='libx265', pix_fmt='yuv420p', crf=23)
+            .overwrite_output()
+            .run_async(pipe_stdin=True)
+        )
+        
+        # 패딩된 프레임 데이터를 ffmpeg 프로세스에 전달
+        for frame in padded_frames:
+            process.stdin.write(
+                frame.tobytes()
+            )
+        
+        # 프로세스 종료
+        process.stdin.close()
+        process.wait()
+        
+        print(f"{tensor_name}가 {output_video_path}에 저장되었습니다.")
+    
+    # feature_rest_tensors 처리 (15개의 텐서)
+    feature_rest_tensors = projected_data.get('feature_rest_tensors')
+    if feature_rest_tensors is None:
+        print("feature_rest_tensors가 projected_data에 없습니다.")
+    else:
+        for idx, tensor in enumerate(feature_rest_tensors):
+            tensor_name = f'feature_rest_tensor_{idx}'
+            
+            # 텐서를 CPU로 이동하고 NumPy 배열로 변환합니다.
+            tensor_np = tensor.detach().cpu().numpy()
+            
+            # 데이터 타입이 uint8인지 확인하고 변환합니다.
+            if tensor_np.dtype != np.uint8:
+                tensor_np = tensor_np.astype(np.uint8)
+            
+            # 텐서의 차원을 확인합니다.
+            if tensor_np.ndim != 4:
+                print(f"{tensor_name} 텐서의 차원이 4가 아닙니다. 건너뜁니다.")
+                continue
+            
+            num_frames, height, width, channels = tensor_np.shape
+            print(f"{tensor_name} 크기: {tensor_np.shape}")
+            
+            # 채널 수에 따른 픽셀 포맷 설정
+            if channels == 1:
+                pix_fmt = 'gray'
+                tensor_np = tensor_np.squeeze(-1)  # 채널 차원 제거
+            elif channels == 3:
+                pix_fmt = 'rgb24'
+            elif channels == 4:
+                pix_fmt = 'rgba'
+            else:
+                print(f"{tensor_name}의 채널 수 {channels}는 지원되지 않습니다. 건너뜁니다.")
+                continue
+            
+            # 모든 프레임에 대해 패딩을 적용합니다.
+            padded_frames = []
+            for frame in tensor_np:
+                padded_frame = pad_image_to_even_dimensions(frame)
+                padded_frames.append(padded_frame)
+            
+            # 패딩된 프레임의 크기를 가져옵니다.
+            padded_height, padded_width = padded_frames[0].shape[:2]
+            print(f"{tensor_name} 패딩 후 크기: {(padded_height, padded_width)}")
+            
+            # 출력 파일 경로 설정
+            output_video_path = os.path.join(output_directory, f"{tensor_name}.mp4")
+            
+            # ffmpeg 프로세스 설정
+            process = (
+                ffmpeg
+                .input('pipe:', format='rawvideo', pix_fmt=pix_fmt, s=f'{padded_width}x{padded_height}')
+                .output(output_video_path, vcodec='libx265', pix_fmt='yuv420p', crf=23)
+                .overwrite_output()
+                .run_async(pipe_stdin=True)
+            )
+            
+            # 패딩된 프레임 데이터를 ffmpeg 프로세스에 전달
+            for frame in padded_frames:
+                process.stdin.write(
+                    frame.tobytes()
+                )
+            
+            # 프로세스 종료
+            process.stdin.close()
+            process.wait()
+            
+            print(f"{tensor_name}가 {output_video_path}에 저장되었습니다.")
+
+def compression_ffv1(projected_data, output_directory):
+    # 출력 디렉토리가 존재하지 않으면 생성합니다.
+    os.makedirs(output_directory, exist_ok=True)
+    
+    # 압축할 텐서 목록
+    tensors_to_process = {
+        'feature_dc_tensor': projected_data.get('feature_dc_tensor'),
+        'scaling_tensor': projected_data.get('scaling_tensor'),
+        'xyz_tensor': projected_data.get('xyz_tensor'),
+        'opacity_tensor': projected_data.get('opacity_tensor'),
+        'rotation_tensor': projected_data.get('rotation_tensor'),
+    }
+    
+    # 지정된 텐서들을 처리합니다.
+    for tensor_name, tensor in tensors_to_process.items():
+        # 텐서를 CPU로 이동하고 NumPy 배열로 변환합니다.
+        tensor_np = tensor.detach().cpu().numpy()
+        
+        # 데이터 타입이 uint16인지 확인하고 변환합니다.
+        if tensor_np.dtype != np.uint16:
+            tensor_np = tensor_np.astype(np.uint16)
+        
+        # 텐서의 차원을 확인합니다.
+        if tensor_np.ndim != 4:
+            print(f"{tensor_name} 텐서의 차원이 4가 아닙니다. 건너뜁니다.")
+            continue
+        
+        num_frames, height, width, channels = tensor_np.shape
+        print(f"{tensor_name} 크기: {tensor_np.shape}")
+        
+        # 채널 수에 따른 픽셀 포맷 설정
+        if channels == 1:
+            pix_fmt = 'gray16le'
+            tensor_np = tensor_np.squeeze(-1)  # 채널 차원 제거
+        elif channels == 3:
+            pix_fmt = 'rgb48le'
+        elif channels == 4:
+            pix_fmt = 'rgba64le'
+        else:
+            print(f"{tensor_name}의 채널 수 {channels}는 지원되지 않습니다. 건너뜁니다.")
+            continue
+        
+        # 출력 파일 경로 설정
+        output_video_path = os.path.join(output_directory, f"{tensor_name}.mkv")
+        
+        # ffmpeg 프로세스 설정
+        process = (
+            ffmpeg
+            .input('pipe:', format='rawvideo', pix_fmt=pix_fmt, s=f'{width}x{height}')
+            .output(output_video_path, format='matroska', vcodec='ffv1', pix_fmt=pix_fmt)
+            .overwrite_output()
+            .run_async(pipe_stdin=True)
+        )
+        
+        # 프레임 데이터를 ffmpeg 프로세스에 전달
+        for frame in tensor_np:
+            process.stdin.write(
+                frame.tobytes()
+            )
+        
+        # 프로세스 종료
+        process.stdin.close()
+        process.wait()
+        
+        print(f"{tensor_name}가 {output_video_path}에 저장되었습니다.")
+    
+    # feature_rest_tensors 처리 (15개의 텐서)
+    feature_rest_tensors = projected_data.get('feature_rest_tensors')
+    if feature_rest_tensors is None:
+        print("feature_rest_tensors가 projected_data에 없습니다.")
+    else:
+        for idx, tensor in enumerate(feature_rest_tensors):
+            print(tensor.shape)
+            tensor_name = f'feature_rest_tensor_{idx}'
+            
+            # 텐서를 CPU로 이동하고 NumPy 배열로 변환합니다.
+            tensor_np = tensor.detach().cpu().numpy()
+            
+            # 데이터 타입이 uint16인지 확인하고 변환합니다.
+            if tensor_np.dtype != np.uint16:
+                tensor_np = tensor_np.astype(np.uint16)
+            
+            # 텐서의 차원을 확인합니다.
+            if tensor_np.ndim != 4:
+                print(f"{tensor_name} 텐서의 차원이 4가 아닙니다. 건너뜁니다.")
+                continue
+            
+            num_frames, height, width, channels = tensor_np.shape
+            print(f"{tensor_name} 크기: {tensor_np.shape}")
+            
+            # 채널 수에 따른 픽셀 포맷 설정
+            if channels == 1:
+                pix_fmt = 'gray16le'
+                tensor_np = tensor_np.squeeze(-1)  # 채널 차원 제거
+            elif channels == 3:
+                pix_fmt = 'rgb48le'
+            elif channels == 4:
+                pix_fmt = 'rgba64le'
+            else:
+                print(f"{tensor_name}의 채널 수 {channels}는 지원되지 않습니다. 건너뜁니다.")
+                continue
+            
+            # 출력 파일 경로 설정
+            output_video_path = os.path.join(output_directory, f"{tensor_name}.mkv")
+            
+            # ffmpeg 프로세스 설정
+            process = (
+                ffmpeg
+                .input('pipe:', format='rawvideo', pix_fmt=pix_fmt, s=f'{width}x{height}')
+                .output(output_video_path, format='matroska', vcodec='ffv1', pix_fmt=pix_fmt)
+                .overwrite_output()
+                .run_async(pipe_stdin=True)
+            )
+            
+            # 프레임 데이터를 ffmpeg 프로세스에 전달
+            for frame in tensor_np:
+                process.stdin.write(
+                    frame.tobytes()
+                )
+            
+            # 프로세스 종료
+            process.stdin.close()
+            process.wait()
+            
+            print(f"{tensor_name}가 {output_video_path}에 저장되었습니다.")
+
+    
+def quantization(tensor, tensor_min=None, tensor_max=None):
+    # 텐서를 CPU로 이동하고 NumPy 배열로 변환합니다.
+    tensor_np = tensor.detach().cpu().numpy()
+
+    # 최소값과 최대값을 계산합니다.
+    if tensor_min is None:
+        tensor_min = tensor_np.min()
+    if tensor_max is None:
+        tensor_max = tensor_np.max()
+
+    # 데이터 범위를 계산합니다.
+    data_range = tensor_max - tensor_min
+    if data_range == 0:
+        data_range = 1e-6  # 0으로 나누는 것을 방지
+
+    # [0, 65535] 범위로 스케일링하여 uint16으로 변환합니다.
+    # quantized_tensor = ((tensor_np - tensor_min) / data_range * 65535).astype(np.uint16)
+    quantized_tensor = ((tensor_np - tensor_min) / data_range * 255).astype(np.uint8)
+
+    return quantized_tensor, tensor_min, tensor_max
+
+def dequantization(quantized_tensor, tensor_min, tensor_max, device):
+    # 데이터 범위를 계산합니다.
+    data_range = tensor_max - tensor_min
+    if data_range == 0:
+        data_range = 1e-6  # 0으로 나누는 것을 방지
+
+    # uint16 데이터를 float32로 복원합니다.
+    # tensor_np = quantized_tensor.astype(np.float32) / 65535 * data_range + tensor_min
+    tensor_np = quantized_tensor.astype(np.float32) / 255 * data_range + tensor_min
+
+    # 텐서를 생성합니다.
+    tensor = torch.from_numpy(tensor_np)
+
+    return tensor.to(device)
+
+def slice(data_values, num_voxels_x, num_voxels_z, linear_indices, num_pixels, device):
+    # 데이터 채널 수 확인
+    num_channels = data_values.shape[1]
+
+    # 이미지 합계 및 카운트 배열 생성
+    image_sums = torch.zeros((num_pixels, num_channels), dtype=torch.float32, device=device)
+    counts = torch.zeros((num_pixels), dtype=torch.float32, device=device)
+
+    # 각 픽셀 위치에 데이터 값을 누적합니다.
+    image_sums.index_add_(0, linear_indices, data_values)
+    counts.index_add_(0, linear_indices, torch.ones_like(linear_indices, dtype=torch.float32))
+
+    # 평균 데이터 값을 계산합니다.
+    counts_mask = counts > 0
+    image_means = torch.zeros_like(image_sums)
+    image_means[counts_mask] = image_sums[counts_mask] / counts[counts_mask].unsqueeze(1)
+
+    # 이미지를 (num_voxels_z, num_voxels_x, 채널 수) 형태로 변환합니다.
+    image_means = image_means.view(num_voxels_z, num_voxels_x, num_channels)
+    
+    return image_means
+    
+def projection(gaussians, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, debug_from, fix_xyz, quantize):
+    # xyz 좌표와 feature_dc, scaling 값을 가져옵니다.
+    # print("===================")
+    xyz = gaussians._xyz  # [N, 3]
+    feature_dc = gaussians._features_dc.squeeze(1)  # [N, 3]
+    feature_rest = gaussians._features_rest  # [N, 15, 3]
+    scaling = gaussians._scaling  # [N, 3]
+    rotation = gaussians._rotation  # [N, 4]
+    opacity = gaussians._opacity  # [N, 1]
+    
+    # 각 데이터의 최소값과 최대값을 계산합니다.
+    feature_dc_min = feature_dc.min(dim=0).values 
+    feature_dc_max = feature_dc.max(dim=0).values 
+    
+    scaling_min = scaling.min(dim=0).values 
+    scaling_max = scaling.max(dim=0).values  
+    
+    opacity_min = opacity.min()
+    opacity_max = opacity.max()
+    
+    rotation_min = rotation.min(dim=0).values
+    rotation_max = rotation.max(dim=0).values  
+    
+    min_vals = xyz.min(dim=0).values 
+    max_vals = xyz.max(dim=0).values 
+    x_min, y_min, z_min = min_vals
+    x_max, y_max, z_max = max_vals
+
+    # 복셀 크기를 설정합니다.
+    voxel_size_x = 0.0035
+    voxel_size_y = 0.0035
+    voxel_size_z = 0.0035
+
+    # 각 축에 대한 복셀 수를 계산합니다.
+    num_voxels_x = torch.ceil((x_max - x_min) / voxel_size_x).long()
+    num_voxels_y = torch.ceil((y_max - y_min) / voxel_size_y).long()
+    num_voxels_z = torch.ceil((z_max - z_min) / voxel_size_z).long()
+    
+    print("===============================")
+    print(f"Number of voxels (x, y, z): {num_voxels_x}, {num_voxels_y}, {num_voxels_z}")
+
+    # 각 점의 복셀 인덱스를 계산합니다.
+    voxel_indices_x = torch.floor((xyz[:, 0] - x_min) / voxel_size_x).long()
+    voxel_indices_y = torch.floor((xyz[:, 1] - y_min) / voxel_size_y).long()
+    voxel_indices_z = torch.floor((xyz[:, 2] - z_min) / voxel_size_z).long()
+
+    # 인덱스가 범위를 벗어나지 않도록 클램핑합니다.
+    voxel_indices_x = voxel_indices_x.clamp(0, num_voxels_x - 1)
+    voxel_indices_y = voxel_indices_y.clamp(0, num_voxels_y - 1)
+    voxel_indices_z = voxel_indices_z.clamp(0, num_voxels_z - 1)
+
+    
+    # with open("./voxel_indices.txt", "w") as f:
+    #     for x, y, z in zip(voxel_indices_x, voxel_indices_y, voxel_indices_z):
+    #         f.write(f"{x.item()}, {y.item()}, {z.item()}\n")
+    # 복셀 인덱스를 결합하여 [N, 3] 형태로 만듭니다.
+    voxel_indices = torch.stack((voxel_indices_x, voxel_indices_y, voxel_indices_z), dim=1)
+    
+     # 각 복셀에 포함된 포인트 수를 계산합니다.
+    unique_voxels, counts = torch.unique(voxel_indices, dim=0, return_counts=True)
+    total_voxels = num_voxels_x * num_voxels_y * num_voxels_z
+    empty_voxels = total_voxels.item() - unique_voxels.size(0)
+    
+    
+    save = True
+
+    if save == True:
+        device = xyz.device
+
+        # 각 데이터를 저장할 리스트를 초기화합니다.
+        feature_dc_images = []
+        scaling_images = []
+        xyz_images = []
+        opacity_images = []
+        rotation_images = []
+        feature_rest_images = [[] for _ in range(15)]  # 15개의 SH 계수에 대한 리스트
+
+        # y축 기준으로 슬라이싱
+        for y in range(num_voxels_y):
+            slice_mask = (voxel_indices[:, 1] == y)  # y축 기준으로 슬라이싱
+
+            # 슬라이스된 점들의 인덱스와 값을 가져옵니다.
+            x_indices = voxel_indices_x[slice_mask]  # [M]
+            z_indices = voxel_indices_z[slice_mask]  # [M]
+            linear_indices = z_indices * num_voxels_x + x_indices  # [M]
+            num_pixels = num_voxels_z * num_voxels_x
+
+            # feature_dc 처리
+            feature_dc_values = feature_dc[slice_mask]  # [M, 3]
+            image_means_dc = slice(
+                data_values=feature_dc_values,
+                num_voxels_x=num_voxels_x,
+                num_voxels_z=num_voxels_z,
+                linear_indices=linear_indices,
+                num_pixels=num_pixels,
+                device=device
+            )
+            feature_dc_images.append(image_means_dc.unsqueeze(0))  # [1, Z, X, 3]
+
+            # scaling 처리
+            scaling_values = scaling[slice_mask]  # [M, 3]
+            image_means_scaling = slice(
+                data_values=scaling_values,
+                num_voxels_x=num_voxels_x,
+                num_voxels_z=num_voxels_z,
+                linear_indices=linear_indices,
+                num_pixels=num_pixels,
+                device=device
+            )
+            scaling_images.append(image_means_scaling.unsqueeze(0))
+
+            # xyz 처리 (x와 z 좌표만 사용)
+            if fix_xyz == False:
+                xyz_values = xyz[slice_mask][:, [0, 2]]  # [M, 2]
+                image_means_xyz = slice(
+                    data_values=xyz_values,
+                    num_voxels_x=num_voxels_x,
+                    num_voxels_z=num_voxels_z,
+                    linear_indices=linear_indices,
+                    num_pixels=num_pixels,
+                    device=device
+                )
+                xyz_images.append(image_means_xyz.unsqueeze(0))
+
+            # opacity 처리
+            opacity_values = opacity[slice_mask]  # [M, 1]
+            image_means_opacity = slice(
+                data_values=opacity_values,
+                num_voxels_x=num_voxels_x,
+                num_voxels_z=num_voxels_z,
+                linear_indices=linear_indices,
+                num_pixels=num_pixels,
+                device=device
+            )
+            opacity_images.append(image_means_opacity.unsqueeze(0))
+
+            # rotation 처리
+            rotation_values = rotation[slice_mask] if slice_mask.sum() > 0 else torch.zeros((0, 4), dtype=rotation.dtype, device=device)
+            image_means_rotation = slice(
+                data_values=rotation_values,
+                num_voxels_x=num_voxels_x,
+                num_voxels_z=num_voxels_z,
+                linear_indices=linear_indices,
+                num_pixels=num_pixels,
+                device=device
+            )
+            rotation_images.append(image_means_rotation.unsqueeze(0))
+
+            # feature_rest 처리 (15개의 SH 계수)
+            feature_rest_values = feature_rest[slice_mask]  # [M, 15, 3]
+            for i in range(15):
+                coeff_values = feature_rest_values[:, i, :]  # [M, 3]
+                image_means_coeff = slice(
+                    data_values=coeff_values,
+                    num_voxels_x=num_voxels_x,
+                    num_voxels_z=num_voxels_z,
+                    linear_indices=linear_indices,
+                    num_pixels=num_pixels,
+                    device=device
+                )
+                feature_rest_images[i].append(image_means_coeff.unsqueeze(0))
+
+        # y축 방향으로 이미지를 쌓습니다.
+        feature_dc_tensor = torch.cat(feature_dc_images, dim=0)  # [Y, Z, X, 3]
+        scaling_tensor = torch.cat(scaling_images, dim=0)        # [Y, Z, X, 3]
+        opacity_tensor = torch.cat(opacity_images, dim=0)        # [Y, Z, X, 1]
+        rotation_tensor = torch.cat(rotation_images, dim=0)      # [Y, Z, X, 4]
+
+        
+        feature_rest_tensors = []
+        for i in range(15):
+            coeff_tensor = torch.cat(feature_rest_images[i], dim=0)  # [Y, Z, X, 3]
+            feature_rest_tensors.append(coeff_tensor)
+            
+        # 필요한 값들을 딕셔너리에 저장합니다.
+        
+        if fix_xyz == False:
+            xyz_tensor = torch.cat(xyz_images, dim=0)
+        else:
+            xyz_tensor = gaussians._xyz
+            print(xyz_tensor.shape)
+        
+        
+        # 양자화 단계
+        if quantize == True:
+            xyz_tensor, xyz_min, xyz_max = quantization(xyz_tensor)
+            feature_dc_tensor, feature_dc_min, feature_dc_max = quantization(feature_dc_tensor)
+            scaling_tensor, scaling_min, scaling_max = quantization(scaling_tensor)
+            opacity_tensor, opacity_min, opacity_max = quantization(opacity_tensor)
+            rotation_tensor, rotation_min, rotation_max = quantization(rotation_tensor)
+
+            quantized_feature_rest = []
+            feature_rest_mins = []
+            feature_rest_maxs = []
+            for tensor in feature_rest_tensors:
+                quantized_tensor, tensor_min, tensor_max = quantization(tensor)
+                quantized_feature_rest.append(quantized_tensor)
+                feature_rest_mins.append(tensor_min)
+                feature_rest_maxs.append(tensor_max)
+
+
+            # 복원 단계
+            xyz_tensor = dequantization(xyz_tensor, xyz_min, xyz_max, device)
+            feature_dc_tensor = dequantization(feature_dc_tensor, feature_dc_min, feature_dc_max, device)
+            scaling_tensor = dequantization(scaling_tensor, scaling_min, scaling_max, device)
+            opacity_tensor = dequantization(opacity_tensor, opacity_min, opacity_max, device)
+            rotation_tensor = dequantization(rotation_tensor, rotation_min, rotation_max, device)
+            
+            feature_rest_tensors = []
+            for idx, quantized_tensor in enumerate(quantized_feature_rest):
+                tensor_min = feature_rest_mins[idx]
+                tensor_max = feature_rest_maxs[idx]
+                restored_tensor = dequantization(quantized_tensor, tensor_min, tensor_max, device)
+                feature_rest_tensors.append(restored_tensor)
+                
+            torch.cuda.empty_cache()
+        print("feature dc :",feature_dc_tensor.shape)
+        print("scaling :",scaling_tensor.shape)
+        print("opacity :",opacity_tensor.shape)
+        print("rotation_tensor :",rotation_tensor.shape)
+        
+        projected_data = {
+            'feature_dc_tensor': feature_dc_tensor,
+            'scaling_tensor': scaling_tensor,
+            'xyz_tensor': xyz_tensor,
+            'opacity_tensor': opacity_tensor,
+            'rotation_tensor': rotation_tensor,
+            'feature_rest_tensors': feature_rest_tensors,
+            'x_min': x_min,
+            'y_min': y_min,
+            'z_min': z_min,
+            'voxel_size_x': voxel_size_x,
+            'voxel_size_y': voxel_size_y,
+            'voxel_size_z': voxel_size_z,
+            'num_voxels_x': num_voxels_x,
+            'num_voxels_y': num_voxels_y,
+            'num_voxels_z': num_voxels_z,
+            'voxel_indices_x': voxel_indices_x,
+            'voxel_indices_y': voxel_indices_y,
+            'voxel_indices_z': voxel_indices_z,
+        }
+            
+        
+        return projected_data
+
+def unprojection(projected_data, gaussians, device):
+    print("=========unprojection=========")
+    # 필요한 텐서들을 가져옵니다.
+    xyz_tensor = projected_data['xyz_tensor']  # [Y, Z, X, 2]
+    # n,3
+    feature_dc_tensor = projected_data['feature_dc_tensor']  # [Y, Z, X, 3]
+    scaling_tensor = projected_data['scaling_tensor']  # [Y, Z, X, 3]
+    opacity_tensor = projected_data['opacity_tensor']  # [Y, Z, X, 1]
+    rotation_tensor = projected_data['rotation_tensor']  # [Y, Z, X, 4]
+    feature_rest_tensors = projected_data['feature_rest_tensors']  # 리스트 형태
+
+    y_min = projected_data['y_min']
+    voxel_size_y = projected_data['voxel_size_y']
+
+    num_voxels_x = projected_data['num_voxels_x']
+    num_voxels_z = projected_data['num_voxels_z']
+
+    # xyz_tensor에서 유효한 복셀 위치를 찾습니다.
+    xyz_mask = torch.any(xyz_tensor != 0, dim=-1)  # [Y, Z, X]
+    print("xyz mask, ", xyz_mask.shape)
+    valid_indices = torch.nonzero(xyz_mask, as_tuple=False)  # [N, 3]
+    del xyz_mask
+    
+    # 유효한 복셀의 인덱스를 사용하여 데이터를 추출합니다.
+    linear_indices = valid_indices[:, 0] * (num_voxels_z * num_voxels_x) + valid_indices[:, 1] * num_voxels_x + valid_indices[:, 2]  # [N]
+
+    del num_voxels_x
+    del num_voxels_z
+    # 각 텐서를 평탄화합니다.
+    xyz_tensor = xyz_tensor.reshape(-1, 2)  # [total_voxels, 2]
+    feature_dc_tensor = feature_dc_tensor.reshape(-1, 3)  # [total_voxels, 3]
+    scaling_tensor = scaling_tensor.reshape(-1, 3)        # [total_voxels, 3]
+    opacity_tensor = opacity_tensor.reshape(-1, 1)        # [total_voxels, 1]
+    rotation_tensor = rotation_tensor.reshape(-1, 4)      # [total_voxels, 4]
+    feature_rest_tensors = [tensor.reshape(-1, 3) for tensor in feature_rest_tensors]  # 리스트 형태
+
+    # 유효한 복셀에서 데이터 추출
+    linear_indices = linear_indices.to(device)
+    xyz_tensor = xyz_tensor[linear_indices]  # [N, 2]
+    feature_dc_tensor = feature_dc_tensor[linear_indices]  # [N, 3]
+    scaling_tensor = scaling_tensor[linear_indices]        # [N, 3]
+    opacity_tensor = opacity_tensor[linear_indices]        # [N, 1]
+    rotation_tensor = rotation_tensor[linear_indices]      # [N, 4]
+    feature_rest_tensors = [tensor[linear_indices] for tensor in feature_rest_tensors]  # 리스트 형태
+    del linear_indices
+    # x, y, z 좌표를 복원합니다.
+    voxel_indices_y = valid_indices[:, 0].to(device)
+    del valid_indices
+
+    # x와 z 좌표는 원래 데이터에서 가져옵니다.
+    x_coords = xyz_tensor[:, 0]
+    z_coords = xyz_tensor[:, 1]
+    # y 좌표는 복셀 인덱스로부터 복원합니다.
+    y_coords = y_min + (voxel_indices_y.float() + 0.5) * voxel_size_y
+    del voxel_indices_y
+    
+    # 최종 xyz 좌표를 생성합니다.
+    xyz_tensor = torch.stack((x_coords, y_coords, z_coords), dim=1)  # [N, 3]
+    del x_coords
+    del z_coords
+    del y_coords
+
+    # 가우시안 모델에 데이터 할당
+    gaussians._xyz = xyz_tensor  # [N, 3]
+    gaussians._features_dc = feature_dc_tensor.unsqueeze(1)  # [N, 1, 3]
+    gaussians._scaling = scaling_tensor  # [N, 3]
+    gaussians._opacity = opacity_tensor  # [N, 1]
+    gaussians._rotation = rotation_tensor  # [N, 4]
+
+    # feature_rest를 [N, 15, 3] 형태로 결합합니다.
+    feature_rest_tensors = torch.stack(feature_rest_tensors, dim=1)  # [N, 15, 3]
+    gaussians._features_rest = feature_rest_tensors  # [N, 15, 3]
+
+    return gaussians
+
+def fix_xyz_unprojection(projected_data, gaussians, device):
+    print("=========fix_xyz_unprojection=========")
+    # 필요한 텐서들을 가져옵니다.
+    xyz_tensor = projected_data['xyz_tensor']
+    print("fix xyz unpro :",xyz_tensor.shape)
+    feature_dc_tensor = projected_data['feature_dc_tensor']  # [Y, Z, X, 3]
+    scaling_tensor = projected_data['scaling_tensor']  # [Y, Z, X, 3]
+    opacity_tensor = projected_data['opacity_tensor']  # [Y, Z, X, 1]
+    rotation_tensor = projected_data['rotation_tensor']  # [Y, Z, X, 4]
+    feature_rest_tensors = projected_data['feature_rest_tensors']  # 리스트 형태
+
+    voxel_indices_x = projected_data['voxel_indices_x']  # [N]
+    voxel_indices_y = projected_data['voxel_indices_y']  # [N]
+    voxel_indices_z = projected_data['voxel_indices_z']  # [N]
+
+    # 각 포인트의 복셀 인덱스를 사용하여 속성 값을 가져옵니다.
+    # 복셀 인덱스를 텐서 인덱스로 사용하기 위해 차원을 확장합니다.
+    voxel_indices_y = voxel_indices_y.to(device).unsqueeze(-1)  # [N, 1]
+    voxel_indices_z = voxel_indices_z.to(device).unsqueeze(-1)  # [N, 1]
+    voxel_indices_x = voxel_indices_x.to(device).unsqueeze(-1)  # [N, 1]
+
+    # 속성 텐서에서 해당 복셀의 속성 값을 가져옵니다.
+    reconstructed_feature_dc = feature_dc_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)  # [N, 3]
+    reconstructed_scaling = scaling_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)        # [N, 3]
+    reconstructed_opacity = opacity_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)        # [N, 1]
+    reconstructed_rotation = rotation_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)      # [N, 4]
+    reconstructed_feature_rest = []
+    
+    for tensor in feature_rest_tensors:
+        value = tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)  # [N, 3]
+        reconstructed_feature_rest.append(value)
+    
+    gaussians._xyz = xyz_tensor
+    gaussians._features_dc = reconstructed_feature_dc.unsqueeze(1)  # [N, 1, 3]
+    gaussians._scaling = reconstructed_scaling  # [N, 3]
+    gaussians._opacity = reconstructed_opacity  # [N, 1]
+    gaussians._rotation = reconstructed_rotation  # [N, 4]
+
+    # feature_rest를 [N, 15, 3] 형태로 결합합니다.
+    reconstructed_feature_rest = torch.stack(reconstructed_feature_rest, dim=1)  # [N, 15, 3]
+    gaussians._features_rest = reconstructed_feature_rest  # [N, 15, 3]
+
+    return gaussians
+
+
+def training_report(reconstructed_gaussians, scene, renderFunc, renderArgs):
+    
+    # 테스트 및 훈련 데이터셋 검증
+    torch.cuda.empty_cache()
+    validation_configs = ({
+        'name': 'test',
+        'cameras' : scene.getTestCameras()
+    }, {
+        'name': 'train',
+        'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]
+    })
+
+
+    for config in validation_configs:
+        if config['cameras'] and len(config['cameras']) > 0:
+            psnr_test = 0.0
+            for idx, viewpoint in enumerate(config['cameras']):
+                # 재구성된 Gaussian 모델로 렌더링
+                image = torch.clamp(renderFunc(viewpoint, reconstructed_gaussians, *renderArgs)["render"], 0.0, 1.0)
+                gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
+                psnr_test += psnr(image, gt_image).mean().double()
+            psnr_test /= len(config['cameras'])
+
+            # PSNR과 L1 Test 값 출력
+            print(f"\nEvaluating {config['name']}: PSNR {psnr_test:.4f}")
+
+    torch.cuda.empty_cache()
+# python .\projection.py -s .\data\gaussian_splatting\tandt_db\tandt\truck --start_checkpoint .\output\lego\point_cloud\iteration_30000\point_cloud.ply
+# python .\projection.py -s ..\data\nerf_synthetic\nerf_synthetic\lego --start_checkpoint .\output\lego\point_cloud\iteration_30000\point_cloud.ply
+
+if __name__ == "__main__":
+    # Set up command line argument parser
+    parser = ArgumentParser(description="Training script parameters")
+    lp = ModelParams(parser)
+    op = OptimizationParams(parser)
+    pp = PipelineParams(parser)
+    parser.add_argument('--ip', type=str, default="127.0.0.1")
+    parser.add_argument('--port', type=int, default=6009)
+    parser.add_argument('--debug_from', type=int, default=-1)
+    parser.add_argument('--detect_anomaly', action='store_true', default=False)
+    parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
+    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
+    parser.add_argument("--quiet", action="store_true")
+    parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
+    parser.add_argument("--start_checkpoint", type=str, default = None)
+    args = parser.parse_args(sys.argv[1:])
+    args.save_iterations.append(args.iterations)
+    
+    print("Optimizing " + args.model_path)
+
+    # Initialize system state (RNG)
+    safe_state(args.quiet)
+
+    # Start GUI server, configure and run training
+    network_gui.init(args.ip, args.port)
+    torch.autograd.set_detect_anomaly(args.detect_anomaly)
+    gaussians = GaussianModel(lp.extract(args).sh_degree)
+    scene = Scene(lp.extract(args), gaussians)
+
+    fix_xyz = True
+    quantize = False
+    gaussians.load_ply(args.start_checkpoint)
+    projected_data = projection(gaussians, op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.debug_from, fix_xyz, quantize)
+
+    output_video_path = './output/lego/point_cloud/iteration_30000/compression_uint8/'
+    # compression_ffv1(projected_data, output_video_path)
+    # compression_h265(projected_data, output_video_path)
+    output_ply_path = './output/lego/point_cloud/iteration_30000/compression_uint8/xyz.ply'
+    xyz_tensor = projected_data['xyz_tensor']
+    # save_ply(xyz_tensor, output_ply_path)
+    
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    if fix_xyz == False:
+        reconstructed_gaussians = unprojection(projected_data, gaussians, device)
+    else:
+        reconstructed_gaussians = fix_xyz_unprojection(projected_data, gaussians, device)
+
+    bg_color = [1, 1, 1] if lp.extract(args).white_background else [0, 0, 0]
+    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
+    training_report(gaussians, scene, render, (pp.extract(args), background))
+    # 재구성된 가우시안을 저장하려면 다음과 같이 저장합니다.
+    if reconstructed_gaussians is not None:
+        reconstructed_gaussians.save_ply('./output/lego/point_cloud/iteration_30000/uint8_fix.ply')
+
+    # All done
+    print("\nTraining complete.")

projection_tensor_test.py

@@ -0,0 +1,223 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import os
+import torch
+from random import randint
+from utils.loss_utils import l1_loss, ssim
+from gaussian_renderer import render, network_gui
+import sys
+from scene import Scene, GaussianModel
+from utils.general_utils import safe_state
+import uuid
+from tqdm import tqdm
+from utils.image_utils import psnr
+from argparse import ArgumentParser, Namespace
+from arguments import ModelParams, PipelineParams, OptimizationParams
+from utils.system_utils import mkdir_p
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+# import open3d as o3d
+import numpy as np
+import cv2
+from collections import Counter
+import pandas as pd
+from PIL import Image
+
+
+def save_ply(gaussians, path):
+    mkdir_p(os.path.dirname(path))
+
+    xyz = gaussians._xyz.detach().cpu().numpy()
+    normals = np.zeros_like(xyz)
+    f_dc = gaussians._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+    f_rest = gaussians._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
+    opacities = gaussians._opacity.detach().cpu().numpy()
+    scale = gaussians._scaling.detach().cpu().numpy()
+    rotation = gaussians._rotation.detach().cpu().numpy()
+
+    dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
+
+    elements = np.empty(xyz.shape[0], dtype=dtype_full)
+    attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
+    elements[:] = list(map(tuple, attributes))
+    el = PlyElement.describe(elements, 'vertex')
+    PlyData([el]).write(path)
+
+def training_report(reconstructed_gaussians, scene, renderFunc, renderArgs):
+    
+    # 테스트 및 훈련 데이터셋 검증
+    torch.cuda.empty_cache()
+    validation_configs = ({
+        'name': 'test',
+        'cameras' : scene.getTestCameras()
+    }, {
+        'name': 'train',
+        'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]
+    })
+
+
+    for config in validation_configs:
+        if config['cameras'] and len(config['cameras']) > 0:
+            psnr_test = 0.0
+            for idx, viewpoint in enumerate(config['cameras']):
+                # 재구성된 Gaussian 모델로 렌더링
+                image = torch.clamp(renderFunc(viewpoint, reconstructed_gaussians, *renderArgs)["render"], 0.0, 1.0)
+                gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
+                psnr_test += psnr(image, gt_image).mean().double()
+            psnr_test /= len(config['cameras'])
+
+            # PSNR과 L1 Test 값 출력
+            print(f"\nEvaluating {config['name']}: PSNR {psnr_test:.4f}")
+
+    torch.cuda.empty_cache()
+
+def projection(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
+    gaussians = GaussianModel(dataset.sh_degree)
+    scene = Scene(dataset, gaussians)
+    bg_color = [1, 1, 1] if lp.extract(args).white_background else [0, 0, 0]
+    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
+    
+    gaussians.load_ply(checkpoint)
+    print("load ply :", gaussians._xyz.shape)
+    print("load ply :", gaussians._xyz)
+    
+    csp_data = gaussians.csp()
+    print("csp :", gaussians._xyz.shape)
+    print("csp :", gaussians._xyz)
+    
+    gaussians.unprojection(csp_data)
+    print("unprojection :", gaussians._xyz.shape)
+    print("unprojection :", gaussians._xyz)
+    
+    gaussians.save_ply('./test/lego/point_cloud/iteration_30000/test.ply')
+    training_report(gaussians, scene, render, (pp.extract(args), background))
+    
+        
+def save_features_dc_tensor(features_dc_tensor, output_dir, slice_axis='z', normalize=True):
+    """
+    Save features_dc_tensor as images along a specified axis.
+
+    Args:
+        features_dc_tensor (torch.Tensor): Tensor of shape [num_voxels_y, num_voxels_z, num_voxels_x, 3]
+        output_dir (str): Directory to save images
+        slice_axis (str): Axis along which to slice ('y', 'z', or 'x')
+        normalize (bool): Whether to normalize tensor values to [0, 1]
+    """
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Ensure tensor is on CPU
+    features_dc_tensor = features_dc_tensor.detach().cpu()
+
+    if slice_axis == 'y':
+        num_slices = features_dc_tensor.shape[0]
+        for y in range(num_slices):
+            # Slice along y-axis: [z, x, 3]
+            image = features_dc_tensor[y]  # [z, x, 3]
+            image = image.numpy()
+
+            if normalize:
+                min_val = image.min()
+                max_val = image.max()
+                if max_val > min_val:
+                    image = (image - min_val) / (max_val - min_val)
+                else:
+                    image = image * 0.0
+
+            # Convert to uint8
+            image_uint8 = (image * 255).astype(np.uint8)
+            pil_image = Image.fromarray(image_uint8)
+
+            image_path = os.path.join(output_dir, f"feature_dc_y_{y:04d}.png")
+            pil_image.save(image_path)
+
+    elif slice_axis == 'z':
+        num_slices = features_dc_tensor.shape[1]
+        for z in range(num_slices):
+            # Slice along z-axis: [y, x, 3]
+            image = features_dc_tensor[:, z, :, :]  # [y, x, 3]
+            image = image.numpy()
+
+            if normalize:
+                min_val = image.min()
+                max_val = image.max()
+                if max_val > min_val:
+                    image = (image - min_val) / (max_val - min_val)
+                else:
+                    image = image * 0.0
+
+            # Convert to uint8
+            image_uint8 = (image * 255).astype(np.uint8)
+            pil_image = Image.fromarray(image_uint8)
+
+            image_path = os.path.join(output_dir, f"feature_dc_z_{z:04d}.png")
+            pil_image.save(image_path)
+
+    elif slice_axis == 'x':
+        num_slices = features_dc_tensor.shape[2]
+        for x in range(num_slices):
+            # Slice along x-axis: [y, z, 3]
+            image = features_dc_tensor[:, :, x, :]  # [y, z, 3]
+            image = image.numpy()
+
+            if normalize:
+                min_val = image.min()
+                max_val = image.max()
+                if max_val > min_val:
+                    image = (image - min_val) / (max_val - min_val)
+                else:
+                    image = image * 0.0
+
+            # Convert to uint8
+            image_uint8 = (image * 255).astype(np.uint8)
+            pil_image = Image.fromarray(image_uint8)
+
+            image_path = os.path.join(output_dir, f"feature_dc_x_{x:04d}.png")
+            pil_image.save(image_path)
+
+    else:
+        raise ValueError("slice_axis must be 'y', 'z', or 'x'.")
+
+    print(f"Saved features_dc_tensor slices along {slice_axis}-axis to {output_dir}")
+
+# python .\projection.py -s .\data\gaussian_splatting\tandt_db\tandt\truck --start_checkpoint .\output\lego\point_cloud\iteration_30000\point_cloud.ply
+# python .\projection_tensor_test.py -s ..\..\data\nerf_synthetic\nerf_synthetic\lego --start_checkpoint ..\output\lego\point_cloud\iteration_30000\point_cloud.ply
+
+if __name__ == "__main__":
+    # Set up command line argument parser
+    parser = ArgumentParser(description="Training script parameters")
+    lp = ModelParams(parser)
+    op = OptimizationParams(parser)
+    pp = PipelineParams(parser)
+    parser.add_argument('--ip', type=str, default="127.0.0.1")
+    parser.add_argument('--port', type=int, default=6009)
+    parser.add_argument('--debug_from', type=int, default=-1)
+    parser.add_argument('--detect_anomaly', action='store_true', default=False)
+    parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
+    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
+    parser.add_argument("--quiet", action="store_true")
+    parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
+    parser.add_argument("--start_checkpoint", type=str, default = None)
+    args = parser.parse_args(sys.argv[1:])
+    args.save_iterations.append(args.iterations)
+    
+    print("Optimizing " + args.model_path)
+
+    # Initialize system state (RNG)
+    safe_state(args.quiet)
+
+    # Start GUI server, configure and run training
+    network_gui.init(args.ip, args.port)
+    torch.autograd.set_detect_anomaly(args.detect_anomaly)
+    projection(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)
+
+    # All done
+    print("\nTraining complete.")

scene/__init__.py

@@ -48,6 +48,7 @@ class Scene:
         else:
             assert False, "Could not recognize scene type!"
             
+
         if not self.loaded_iter:
             with open(scene_info.ply_path, 'rb') as src_file, open(os.path.join(self.model_path, "input.ply") , 'wb') as dest_file:
                 dest_file.write(src_file.read())

scene/gaussian_model.py

@@ -113,7 +113,7 @@ class GaussianModel:
     
     @property
     def get_features(self):
-        features_dc = self._features_dc
+        features_dc = self._features_dc#.unsqueeze(1)
         features_rest = self._features_rest
         return torch.cat((features_dc, features_rest), dim=1)
     
@@ -146,30 +146,198 @@ class GaussianModel:
         if self.active_sh_degree < self.max_sh_degree:
             self.active_sh_degree += 1
             
+    def csp(self):
+        self._features_dc = self._features_dc.squeeze(1)  # [N, 3]
+
+        min_vals = self._xyz.min(dim=0).values
+        max_vals = self._xyz.max(dim=0).values
+        x_min, y_min, z_min = min_vals
+        x_max, y_max, z_max = max_vals
+
+        num_voxels_x = torch.ceil((x_max - x_min) / self.voxel_size).long()
+        num_voxels_y = torch.ceil((y_max - y_min) / self.voxel_size).long()
+        num_voxels_z = torch.ceil((z_max - z_min) / self.voxel_size).long()
+
+        voxel_indices_x = torch.floor((self._xyz[:, 0] - x_min) / self.voxel_size).long().clamp(0, num_voxels_x - 1)
+        voxel_indices_y = torch.floor((self._xyz[:, 1] - y_min) / self.voxel_size).long().clamp(0, num_voxels_y - 1)
+        voxel_indices_z = torch.floor((self._xyz[:, 2] - z_min) / self.voxel_size).long().clamp(0, num_voxels_z - 1)
+
+        # 전체 포인트에 대한 선형 인덱스 계산
+        linear_indices_all = voxel_indices_y * (num_voxels_z * num_voxels_x) + voxel_indices_z * num_voxels_x + voxel_indices_x
+
+        num_voxels_total = num_voxels_y * num_voxels_z * num_voxels_x
+
+        # 공통 로직을 수행하는 헬퍼 함수
+        def accumulate_and_average(data_values, channels):
+            """
+            data_values: [N, channels]
+            channels: int, number of channels per voxel
+            """
+            image_sums = torch.zeros((num_voxels_total, channels), dtype=torch.float32, device='cuda')
+            counts = torch.zeros((num_voxels_total), dtype=torch.float32, device='cuda')
+
+            image_sums.index_add_(0, linear_indices_all, data_values)
+            counts.index_add_(0, linear_indices_all, torch.ones_like(linear_indices_all, dtype=torch.float32))
+
+            counts_mask = counts > 0
+            image_means = torch.zeros_like(image_sums)
+            image_means[counts_mask] = image_sums[counts_mask] / counts[counts_mask].unsqueeze(1)
+            return image_means
+
+        # features_dc 처리: [N, 3] -> [Y, Z, X, 3]
+        data_values_dc = self._features_dc  # [N, 3]
+        image_means_dc = accumulate_and_average(data_values_dc, 3)
+        features_dc_tensor = image_means_dc.view(num_voxels_y, num_voxels_z, num_voxels_x, 3)
+
+        # scaling 처리: [N, 3] -> [Y, Z, X, 3]
+        data_values_scaling = self._scaling  # [N, 3]
+        image_means_scaling = accumulate_and_average(data_values_scaling, 3)
+        scaling_tensor = image_means_scaling.view(num_voxels_y, num_voxels_z, num_voxels_x, 3)
+
+        # opacity 처리: [N, 1] -> [Y, Z, X, 1]
+        data_values_opacity = self._opacity  # [N, 1]
+        image_means_opacity = accumulate_and_average(data_values_opacity, 1)
+        opacity_tensor = image_means_opacity.view(num_voxels_y, num_voxels_z, num_voxels_x, 1)
+
+        # rotation 처리: [N, 4] -> [Y, Z, X, 4]
+        data_values_rotation = self._rotation  # [N, 4]
+        image_means_rotation = accumulate_and_average(data_values_rotation, 4)
+        rotation_tensor = image_means_rotation.view(num_voxels_y, num_voxels_z, num_voxels_x, 4)
+
+        # features_rest 처리: [N, 15, 3] -> 리스트로 [Y, Z, X, 3] 형태의 15개 텐서
+        # 각각의 SH 계수를 별도로 처리한 뒤 스택
+        data_values_rest = self._features_rest  # [N, 15, 3]
+        features_rest_tensors = []
+        for i in range(15):
+            coeff_values = data_values_rest[:, i, :]  # [N, 3]
+            image_means_coeff = accumulate_and_average(coeff_values, 3)
+            coeff_tensor = image_means_coeff.view(num_voxels_y, num_voxels_z, num_voxels_x, 3)
+            features_rest_tensors.append(coeff_tensor)
+
+        csp_data = {
+            'features_dc_tensor': features_dc_tensor,
+            'scaling_tensor': scaling_tensor,
+            'opacity_tensor': opacity_tensor,
+            'rotation_tensor': rotation_tensor,
+            'features_rest_tensors': features_rest_tensors,
+            'voxel_indices_x': voxel_indices_x,
+            'voxel_indices_y': voxel_indices_y,
+            'voxel_indices_z': voxel_indices_z
+        }
+
+        return csp_data
+    
+    
+    def unprojection(self, csp_data):
+        features_dc_tensor = csp_data['features_dc_tensor']  # [Y, Z, X, 3]
+        scaling_tensor = csp_data['scaling_tensor']  # [Y, Z, X, 3]
+        opacity_tensor = csp_data['opacity_tensor']  # [Y, Z, X, 1]
+        rotation_tensor = csp_data['rotation_tensor']  # [Y, Z, X, 4]
+        features_rest_tensors = csp_data['features_rest_tensors']  # 리스트 형태
+
+        voxel_indices_x = csp_data['voxel_indices_x']  # [N]
+        voxel_indices_y = csp_data['voxel_indices_y']  # [N]
+        voxel_indices_z = csp_data['voxel_indices_z']  # [N]
+
+        # 각 포인트의 복셀 인덱스를 사용하여 속성 값을 가져옵니다.
+        # 복셀 인덱스를 텐서 인덱스로 사용하기 위해 차원을 확장합니다.
+        voxel_indices_y = voxel_indices_y # [N ]
+        voxel_indices_z = voxel_indices_z # [N ]
+        voxel_indices_x = voxel_indices_x# [N]
+
+        
+        # print("feature_dc:", features_dc_tensor.shape)        
+        # print("scaling:", scaling_tensor.shape)           
+        # print("opacity:", opacity_tensor.shape)             
+        # print("rotation:", rotation_tensor.shape)          
+
+        # 속성 텐서에서 해당 복셀의 속성 값을 가져옵니다.
+        reconstructed_feature_dc = features_dc_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].unsqueeze(1) # [N, 3]
+        reconstructed_scaling = scaling_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)        # [N, 3]
+        reconstructed_opacity = opacity_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x]       # [N, 1]
+        reconstructed_rotation = rotation_tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)      # [N, 4]
+        reconstructed_feature_rest = []
+        
+        for tensor in features_rest_tensors:
+            value = tensor[voxel_indices_y, voxel_indices_z, voxel_indices_x].squeeze(1)  # [N, 3]
+            reconstructed_feature_rest.append(value)
+        
+        reconstructed_feature_rest = torch.stack(reconstructed_feature_rest, dim=1)  # [N, 15, 3]
+
+        
+        # 모든 텐서 shape 출력 및 확인
+        # print("reconstructed_feature_dc:", reconstructed_feature_dc.shape)     
+        # print("self._features_dc:", self._features_dc.shape)
+        # print("reconstructed_scaling:", reconstructed_scaling.shape)         
+        # print("reconstructed_opacity:", reconstructed_opacity.shape)            
+        # print("reconstructed_rotation:", reconstructed_rotation.shape)            
+        # print("reconstructed_feature_rest:", reconstructed_feature_rest.shape) 
+        # self._features_dc = reconstructed_feature_dc
+        # self._scaling = reconstructed_scaling
+        # self._opacity = reconstructed_opacity
+        # self._rotation = reconstructed_rotation
+        # self._features_rest = reconstructed_feature_rest          
+
+
     def create_from_pcd(self, pcd : BasicPointCloud, cam_infos : int, spatial_lr_scale : float):
         self.spatial_lr_scale = spatial_lr_scale
         fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
         fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
         features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()
-        features[:, :3, 0 ] = fused_color
+        features[:, :3, 0] = fused_color
         features[:, 3:, 1:] = 0.0
 
-        print("Number of points at initialisation : ", fused_point_cloud.shape[0])
+        # print("초기 포인트 개수:", fused_point_cloud.shape[0])
 
-        dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001)
+        # 복셀 크기 설정
-        scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3)
+        self.voxel_size = 0.01
+
+        # 포인트를 복셀로 양자화
+        voxel_indices = torch.floor(fused_point_cloud / self.voxel_size).long()
+        unique_voxels, self.inverse_indices = torch.unique(voxel_indices, return_inverse=True, dim=0)
+        # print(unique_voxels.shape) 99740, 3
+        # print(inverse_indices.shape) 100000
+        # 고유 복셀 개수
+        num_voxels = unique_voxels.shape[0]
+
+        # 복셀 단위 속성을 초기화
+        voxel_features = torch.zeros((num_voxels, features.shape[1], features.shape[2]), device='cuda')
+        voxel_counts = torch.zeros(num_voxels, device='cuda') # 각 복셀에 몇개의 포인트가 포함되는지
+        
+
+        # 복셀별 속성 합산 및 개수 계산
+        voxel_feature_sum = torch.zeros_like(voxel_features)
+
+        # 합산
+        voxel_feature_sum.index_add_(0, self.inverse_indices, features)
+        voxel_counts.index_add_(0, self.inverse_indices, torch.ones(features.shape[0], device='cuda'))
+        # print(voxel_counts)
+
+        # 0으로 나누는 것을 방지
+        voxel_counts = voxel_counts.unsqueeze(1).unsqueeze(2).clamp_min(1)
+
+        # 복셀별 속성 평균 계산
+        voxel_features = voxel_feature_sum / voxel_counts
+
+        # 평균 속성을 포인트에 재할당
+        features = voxel_features[self.inverse_indices]
+
+        # 기존 초기화 과정 유지
+        dist2 = torch.clamp_min(distCUDA2(fused_point_cloud), 1e-7)
+        scales = torch.log(torch.sqrt(dist2))[..., None].repeat(1, 3)
         rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda")
         rots[:, 0] = 1
 
         opacities = self.inverse_opacity_activation(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))
 
         self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
-        self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True))
+        self._features_dc = nn.Parameter(features[:, :, 0:1].transpose(1, 2).contiguous().requires_grad_(True))
-        self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True))
+        self._features_rest = nn.Parameter(features[:, :, 1:].transpose(1, 2).contiguous().requires_grad_(True))
         self._scaling = nn.Parameter(scales.requires_grad_(True))
         self._rotation = nn.Parameter(rots.requires_grad_(True))
         self._opacity = nn.Parameter(opacities.requires_grad_(True))
-        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
+        self.max_radii2D = torch.zeros((self._xyz.shape[0]), device="cuda")
+        
         self.exposure_mapping = {cam_info.image_name: idx for idx, cam_info in enumerate(cam_infos)}
         self.pretrained_exposures = None
         exposure = torch.eye(3, 4, device="cuda")[None].repeat(len(cam_infos), 1, 1)
@@ -370,7 +538,6 @@ class GaussianModel:
             extension_tensor = tensors_dict[group["name"]]
             stored_state = self.optimizer.state.get(group['params'][0], None)
             if stored_state is not None:
-
                 stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0)
                 stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0)
 

train.py

@@ -108,6 +108,10 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
 
         bg = torch.rand((3), device="cuda") if opt.random_background else background
 
+        # slice 하기
+        csp_data = gaussians.csp()
+        gaussians.unprojection(csp_data)
+        
         render_pkg = render(viewpoint_cam, gaussians, pipe, bg, use_trained_exp=dataset.train_test_exp, separate_sh=SPARSE_ADAM_AVAILABLE)
         image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
 
@@ -261,7 +265,7 @@ if __name__ == "__main__":
     parser.add_argument('--port', type=int, default=6009)
     parser.add_argument('--debug_from', type=int, default=-1)
     parser.add_argument('--detect_anomaly', action='store_true', default=False)
-    parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
+    parser.add_argument("--test_iterations", nargs="+", type=int, default=[100, 200, 500, 700, 1_000, 7_000, 30_000])
     parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
     parser.add_argument("--quiet", action="store_true")
     parser.add_argument('--disable_viewer', action='store_true', default=False)
@@ -283,3 +287,4 @@ if __name__ == "__main__":
 
     # All done
     print("\nTraining complete.")
+    # python train.py -s ../../data/nerf_synthetic/nerf_synthetic/lego