mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-06-26 18:18:11 +00:00
581 lines
28 KiB
Python
581 lines
28 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import torch
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import numpy as np
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from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation
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from torch import nn
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import os
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import json
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from utils.system_utils import mkdir_p
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from plyfile import PlyData, PlyElement
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from utils.sh_utils import RGB2SH
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from simple_knn._C import distCUDA2
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from utils.graphics_utils import BasicPointCloud
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from utils.general_utils import strip_symmetric, build_scaling_rotation
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from scene.xy_utils import storePly
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try:
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from diff_gaussian_rasterization import SparseGaussianAdam
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except:
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pass
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class GaussianModel:
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def setup_functions(self):
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def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
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L = build_scaling_rotation(scaling_modifier * scaling, rotation)
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actual_covariance = L @ L.transpose(1, 2)
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symm = strip_symmetric(actual_covariance)
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return symm
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self.scaling_activation = torch.exp
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self.scaling_inverse_activation = torch.log
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self.covariance_activation = build_covariance_from_scaling_rotation
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self.opacity_activation = torch.sigmoid
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self.inverse_opacity_activation = inverse_sigmoid
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self.rotation_activation = torch.nn.functional.normalize
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def __init__(self, sh_degree, optimizer_type="default"):
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self.active_sh_degree = 0
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self.optimizer_type = optimizer_type
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self.max_sh_degree = sh_degree
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self._xyz = torch.empty(0)
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self._features_dc = torch.empty(0)
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self._features_rest = torch.empty(0)
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self._scaling = torch.empty(0)
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self._rotation = torch.empty(0)
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self._opacity = torch.empty(0)
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self.max_radii2D = torch.empty(0)
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self.xyz_gradient_accum = torch.empty(0)
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self.denom = torch.empty(0)
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self.optimizer = None
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self.percent_dense = 0
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self.spatial_lr_scale = 0
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self.setup_functions()
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self.skybox_points = 0
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self.skybox_locked = True
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def capture(self):
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return (
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self.active_sh_degree,
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self._xyz,
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self._features_dc,
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self._features_rest,
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self._scaling,
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self._rotation,
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self._opacity,
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self.max_radii2D,
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self.xyz_gradient_accum,
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self.denom,
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self.optimizer.state_dict(),
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self.spatial_lr_scale,
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)
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def restore(self, model_args, training_args):
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(self.active_sh_degree,
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self._xyz,
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self._features_dc,
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self._features_rest,
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self._scaling,
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self._rotation,
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self._opacity,
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self.max_radii2D,
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xyz_gradient_accum,
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denom,
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opt_dict,
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self.spatial_lr_scale) = model_args
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self.training_setup(training_args)
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self.xyz_gradient_accum = xyz_gradient_accum
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self.denom = denom
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self.optimizer.load_state_dict(opt_dict)
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@property
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def get_scaling(self):
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return self.scaling_activation(self._scaling)
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@property
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def get_rotation(self):
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return self.rotation_activation(self._rotation)
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@property
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def get_xyz(self):
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return self._xyz
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@property
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def get_features(self):
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features_dc = self._features_dc
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features_rest = self._features_rest
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return torch.cat((features_dc, features_rest), dim=1)
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@property
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def get_features_dc(self):
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return self._features_dc
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@property
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def get_features_rest(self):
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return self._features_rest
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@property
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def get_opacity(self):
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return self.opacity_activation(self._opacity)
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@property
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def get_exposure(self):
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return self._exposure
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def get_exposure_from_name(self, image_name):
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if self.pretrained_exposures is None:
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return self._exposure[self.exposure_mapping[image_name]]
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else:
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return self.pretrained_exposures[image_name]
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def get_covariance(self, scaling_modifier = 1):
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return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation)
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def oneupSHdegree(self):
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if self.active_sh_degree < self.max_sh_degree:
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self.active_sh_degree += 1
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def create_from_pcd(self,
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pcd : BasicPointCloud,
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cam_infos : int,
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spatial_lr_scale : float,
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addition_points: int,
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scaffold_file: str,
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bounds_file: str,
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skybox_locked: bool):
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# if addition_points > 0:
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# self.skybox_points = addition_points # 原主代码中控制 skybox_points 更新的逻辑
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# groundbox_points = addition_points // 2
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# skybox_points = addition_points - groundbox_points
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self.skybox_points = addition_points
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skybox_points = addition_points
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self.skybox_locked = skybox_locked
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self.spatial_lr_scale = spatial_lr_scale
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xyz_np = np.asarray(pcd.points)
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xyz = torch.tensor(xyz_np).float().cuda() # [N,3] use xyz replace fused_point_cloud
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fused_color = torch.tensor(np.asarray(pcd.colors)).float().cuda() # [N,3] from 0 to 1
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# segment ground plane
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from utils.points_utils import fit_ground_plane
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plane_model, inliers = fit_ground_plane(xyz_np, threshold=0.001)
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inliers = np.array(inliers)
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ground_points = xyz_np[inliers]
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ground_center = ground_points.mean(axis=0)
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ground_radius = np.linalg.norm(ground_points - ground_center, axis=1).max() # need-check
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if scaffold_file != "" and skybox_points > 0: # TODO: load scaffold_file
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print(f"Overriding skybox_points: loading skybox from scaffold_file: {scaffold_file}")
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skybox_points = 0
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if skybox_points > 0:
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radius = ground_radius
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mean = torch.tensor(ground_center).float().cuda()
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theta = (2.0 * torch.pi * torch.rand(skybox_points, device="cuda")).float() # torch.rand generate [0,1)
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phi = (torch.arccos(1.0 - 1.4 * torch.rand(skybox_points, device="cuda"))).float() # arc cos [-0.4,1] --> 角度 [0,110]
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skybox_xyz = torch.zeros((skybox_points, 3))
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skybox_xyz[:, 0] = radius * 5 * torch.cos(theta)*torch.sin(phi) # 5 * radius
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skybox_xyz[:, 1] = radius * 5 * torch.sin(theta)*torch.sin(phi)
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skybox_xyz[:, 2] = radius * 5 * torch.cos(phi)
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normal = torch.tensor(plane_model[:3], dtype=torch.float32)
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up = torch.tensor([0.0, 0.0, 1.0])
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from utils.points_utils import create_rotation_matrix
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R = create_rotation_matrix(up, normal)
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R = torch.from_numpy(R).float()
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skybox_xyz = (R@skybox_xyz.T).T
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skybox_xyz += mean.cpu() # put points in the center of the scene
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xyz = torch.concat((skybox_xyz.cuda(), xyz))
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fused_color = torch.concat((torch.ones((skybox_points, 3)).cuda(), fused_color))
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fused_color[:skybox_points,0] *= 0.7
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fused_color[:skybox_points,1] *= 0.8
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fused_color[:skybox_points,2] *= 0.95
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groundbox_points = 0
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if groundbox_points > 0:
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radius = ground_radius
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mean = torch.tensor(ground_center).float().cuda()
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a, b, c, d = plane_model
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theta = 2.0 * torch.pi * torch.rand(groundbox_points, device="cuda") # 角度 [0, 2pi)
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r = radius * torch.sqrt(torch.rand(groundbox_points, device="cuda")) # 半径范围 [0, 2*radius]
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groundbox_xyz = torch.zeros((groundbox_points, 3))
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groundbox_xyz[:, 0] = r * torch.cos(theta)
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groundbox_xyz[:, 1] = r * torch.sin(theta)
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groundbox_xyz[:, 2] = (-a * groundbox_xyz[:, 0] - b * groundbox_xyz[:, 1] - d) / c
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groundbox_xyz += mean.cpu()
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groundbox_color = torch.ones((groundbox_points, 3), device="cuda") * torch.tensor([0.5, 0.5, 0.5], device="cuda")
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xyz = torch.concat((groundbox_xyz.cuda(), xyz), dim=0)
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fused_color = torch.concat((groundbox_color.cuda(), fused_color), dim=0)
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debug_xy = True
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if debug_xy:
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folder = "/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/outputs_debug"
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sky_ply_path = os.path.join(folder, "skybox_scene_init.ply")
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storePly(sky_ply_path, xyz.cpu(), fused_color.cpu()*255)
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print("save sky and groud init ply in: ", sky_ply_path)
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# fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda()
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# fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda())
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features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda()
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features[:, :3, 0 ] = RGB2SH(fused_color)
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features[:, 3:, 1:] = 0.0
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# dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001)
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dist2 = torch.clamp_min(distCUDA2(xyz), 0.0000001) # shape [],to caculate the
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if scaffold_file == "" and skybox_points > 0:
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dist2[:skybox_points] *= 10 # sky points * 10 扩大每个高斯最近
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dist2[skybox_points:] = torch.clamp_max(dist2[skybox_points:], 10) # 使得场景内高斯点的距离小于10
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scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3)
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rots = torch.zeros((xyz.shape[0], 4), device="cuda")
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rots[:, 0] = 1
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# opacities = self.inverse_opacity_activation(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))
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if scaffold_file == "" and skybox_points > 0:
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opacities = self.inverse_opacity_activation(0.02 * torch.ones((xyz.shape[0], 1), dtype=torch.float, device="cuda"))
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opacities[:skybox_points] = 0.7 # sky 0.02 other 0.7
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else:
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opacities = self.inverse_opacity_activation(0.01 * torch.ones((xyz.shape[0], 1), dtype=torch.float, device="cuda"))
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features_dc = features[:,:,0:1].transpose(1, 2).contiguous()
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features_rest = features[:,:,1:].transpose(1, 2).contiguous()
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self.scaffold_points = None
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if scaffold_file != "":
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print("TODO:load scaffold_file")
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self._xyz = nn.Parameter(xyz.requires_grad_(True))
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self._features_dc = nn.Parameter(features_dc.requires_grad_(True))
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self._features_rest = nn.Parameter(features_rest.requires_grad_(True))
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self._scaling = nn.Parameter(scales.requires_grad_(True))
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self._rotation = nn.Parameter(rots.requires_grad_(True))
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self._opacity = nn.Parameter(opacities.requires_grad_(True))
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self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
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self.exposure_mapping = {cam_info.image_name: idx for idx, cam_info in enumerate(cam_infos)}
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self.pretrained_exposures = None
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exposure = torch.eye(3, 4, device="cuda")[None].repeat(len(cam_infos), 1, 1)
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self._exposure = nn.Parameter(exposure.requires_grad_(True))
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print("Number of points at initialisation : ", self._xyz.shape[0])
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def training_setup(self, training_args):
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self.percent_dense = training_args.percent_dense
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self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
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self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
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l = [
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{'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"},
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{'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"},
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{'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"},
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{'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"},
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{'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"},
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{'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"}
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]
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if self.optimizer_type == "default":
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self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
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elif self.optimizer_type == "sparse_adam":
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try:
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self.optimizer = SparseGaussianAdam(l, lr=0.0, eps=1e-15)
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except:
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# A special version of the rasterizer is required to enable sparse adam
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self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
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self.exposure_optimizer = torch.optim.Adam([self._exposure])
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self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale,
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lr_final=training_args.position_lr_final*self.spatial_lr_scale,
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lr_delay_mult=training_args.position_lr_delay_mult,
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max_steps=training_args.position_lr_max_steps)
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self.exposure_scheduler_args = get_expon_lr_func(training_args.exposure_lr_init, training_args.exposure_lr_final,
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lr_delay_steps=training_args.exposure_lr_delay_steps,
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lr_delay_mult=training_args.exposure_lr_delay_mult,
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max_steps=training_args.iterations)
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def update_learning_rate(self, iteration):
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''' Learning rate scheduling per step '''
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if self.pretrained_exposures is None:
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for param_group in self.exposure_optimizer.param_groups:
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param_group['lr'] = self.exposure_scheduler_args(iteration)
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for param_group in self.optimizer.param_groups:
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if param_group["name"] == "xyz":
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lr = self.xyz_scheduler_args(iteration)
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param_group['lr'] = lr
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return lr
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def construct_list_of_attributes(self):
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l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
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# All channels except the 3 DC
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for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
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l.append('f_dc_{}'.format(i))
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for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]):
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l.append('f_rest_{}'.format(i))
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l.append('opacity')
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for i in range(self._scaling.shape[1]):
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l.append('scale_{}'.format(i))
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for i in range(self._rotation.shape[1]):
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l.append('rot_{}'.format(i))
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return l
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def save_ply(self, path):
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mkdir_p(os.path.dirname(path))
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xyz = self._xyz.detach().cpu().numpy()
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normals = np.zeros_like(xyz)
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f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
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f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
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opacities = self._opacity.detach().cpu().numpy()
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scale = self._scaling.detach().cpu().numpy()
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rotation = self._rotation.detach().cpu().numpy()
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dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
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elements = np.empty(xyz.shape[0], dtype=dtype_full)
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attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
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elements[:] = list(map(tuple, attributes))
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el = PlyElement.describe(elements, 'vertex')
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PlyData([el]).write(path)
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def reset_opacity(self):
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opacities_new = self.inverse_opacity_activation(torch.min(self.get_opacity, torch.ones_like(self.get_opacity)*0.01))
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optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity")
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self._opacity = optimizable_tensors["opacity"]
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def load_ply(self, path, use_train_test_exp = False):
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plydata = PlyData.read(path)
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if use_train_test_exp:
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exposure_file = os.path.join(os.path.dirname(path), os.pardir, os.pardir, "exposure.json")
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if os.path.exists(exposure_file):
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with open(exposure_file, "r") as f:
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exposures = json.load(f)
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self.pretrained_exposures = {image_name: torch.FloatTensor(exposures[image_name]).requires_grad_(False).cuda() for image_name in exposures}
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print(f"Pretrained exposures loaded.")
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else:
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print(f"No exposure to be loaded at {exposure_file}")
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self.pretrained_exposures = None
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xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
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np.asarray(plydata.elements[0]["y"]),
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np.asarray(plydata.elements[0]["z"])), axis=1)
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opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
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features_dc = np.zeros((xyz.shape[0], 3, 1))
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features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
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features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
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features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
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extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
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extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
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assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3
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features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
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for idx, attr_name in enumerate(extra_f_names):
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features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
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# Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC)
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features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))
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scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
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scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
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|
scales = np.zeros((xyz.shape[0], len(scale_names)))
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for idx, attr_name in enumerate(scale_names):
|
|
scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
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|
|
|
rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
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|
rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
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|
rots = np.zeros((xyz.shape[0], len(rot_names)))
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|
for idx, attr_name in enumerate(rot_names):
|
|
rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
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|
|
|
self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
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|
self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
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|
self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
|
|
self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
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|
self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
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|
self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True))
|
|
|
|
self.active_sh_degree = self.max_sh_degree
|
|
|
|
def replace_tensor_to_optimizer(self, tensor, name):
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|
optimizable_tensors = {}
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|
for group in self.optimizer.param_groups:
|
|
if group["name"] == name:
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|
stored_state = self.optimizer.state.get(group['params'][0], None)
|
|
stored_state["exp_avg"] = torch.zeros_like(tensor)
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|
stored_state["exp_avg_sq"] = torch.zeros_like(tensor)
|
|
|
|
del self.optimizer.state[group['params'][0]]
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|
group["params"][0] = nn.Parameter(tensor.requires_grad_(True))
|
|
self.optimizer.state[group['params'][0]] = stored_state
|
|
|
|
optimizable_tensors[group["name"]] = group["params"][0]
|
|
return optimizable_tensors
|
|
|
|
def _prune_optimizer(self, mask):
|
|
optimizable_tensors = {}
|
|
for group in self.optimizer.param_groups:
|
|
stored_state = self.optimizer.state.get(group['params'][0], None)
|
|
if stored_state is not None:
|
|
stored_state["exp_avg"] = stored_state["exp_avg"][mask]
|
|
stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]
|
|
|
|
del self.optimizer.state[group['params'][0]]
|
|
group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
|
|
self.optimizer.state[group['params'][0]] = stored_state
|
|
|
|
optimizable_tensors[group["name"]] = group["params"][0]
|
|
else:
|
|
group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
|
|
optimizable_tensors[group["name"]] = group["params"][0]
|
|
return optimizable_tensors
|
|
|
|
def prune_points(self, mask):
|
|
valid_points_mask = ~mask
|
|
optimizable_tensors = self._prune_optimizer(valid_points_mask)
|
|
|
|
self._xyz = optimizable_tensors["xyz"]
|
|
self._features_dc = optimizable_tensors["f_dc"]
|
|
self._features_rest = optimizable_tensors["f_rest"]
|
|
self._opacity = optimizable_tensors["opacity"]
|
|
self._scaling = optimizable_tensors["scaling"]
|
|
self._rotation = optimizable_tensors["rotation"]
|
|
|
|
self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask]
|
|
|
|
self.denom = self.denom[valid_points_mask]
|
|
self.max_radii2D = self.max_radii2D[valid_points_mask]
|
|
self.tmp_radii = self.tmp_radii[valid_points_mask]
|
|
|
|
def cat_tensors_to_optimizer(self, tensors_dict):
|
|
optimizable_tensors = {}
|
|
for group in self.optimizer.param_groups:
|
|
assert len(group["params"]) == 1
|
|
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)
|
|
|
|
del self.optimizer.state[group['params'][0]]
|
|
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
|
|
self.optimizer.state[group['params'][0]] = stored_state
|
|
|
|
optimizable_tensors[group["name"]] = group["params"][0]
|
|
else:
|
|
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
|
|
optimizable_tensors[group["name"]] = group["params"][0]
|
|
|
|
return optimizable_tensors
|
|
|
|
def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_tmp_radii):
|
|
d = {"xyz": new_xyz,
|
|
"f_dc": new_features_dc,
|
|
"f_rest": new_features_rest,
|
|
"opacity": new_opacities,
|
|
"scaling" : new_scaling,
|
|
"rotation" : new_rotation}
|
|
|
|
optimizable_tensors = self.cat_tensors_to_optimizer(d)
|
|
self._xyz = optimizable_tensors["xyz"]
|
|
self._features_dc = optimizable_tensors["f_dc"]
|
|
self._features_rest = optimizable_tensors["f_rest"]
|
|
self._opacity = optimizable_tensors["opacity"]
|
|
self._scaling = optimizable_tensors["scaling"]
|
|
self._rotation = optimizable_tensors["rotation"]
|
|
|
|
self.tmp_radii = torch.cat((self.tmp_radii, new_tmp_radii))
|
|
self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
|
|
self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda")
|
|
self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
|
|
|
|
def densify_and_split(self, grads, grad_threshold, scene_extent, N=2):
|
|
n_init_points = self.get_xyz.shape[0]
|
|
# Extract points that satisfy the gradient condition
|
|
padded_grad = torch.zeros((n_init_points), device="cuda")
|
|
padded_grad[:grads.shape[0]] = grads.squeeze()
|
|
selected_pts_mask = torch.where(padded_grad >= grad_threshold, True, False)
|
|
selected_pts_mask = torch.logical_and(selected_pts_mask,
|
|
torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent)
|
|
|
|
stds = self.get_scaling[selected_pts_mask].repeat(N,1)
|
|
means =torch.zeros((stds.size(0), 3),device="cuda")
|
|
samples = torch.normal(mean=means, std=stds)
|
|
rots = build_rotation(self._rotation[selected_pts_mask]).repeat(N,1,1)
|
|
new_xyz = torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1) + self.get_xyz[selected_pts_mask].repeat(N, 1)
|
|
new_scaling = self.scaling_inverse_activation(self.get_scaling[selected_pts_mask].repeat(N,1) / (0.8*N))
|
|
new_rotation = self._rotation[selected_pts_mask].repeat(N,1)
|
|
new_features_dc = self._features_dc[selected_pts_mask].repeat(N,1,1)
|
|
new_features_rest = self._features_rest[selected_pts_mask].repeat(N,1,1)
|
|
new_opacity = self._opacity[selected_pts_mask].repeat(N,1)
|
|
new_tmp_radii = self.tmp_radii[selected_pts_mask].repeat(N)
|
|
|
|
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_tmp_radii)
|
|
|
|
prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool)))
|
|
self.prune_points(prune_filter)
|
|
|
|
def densify_and_clone(self, grads, grad_threshold, scene_extent):
|
|
# Extract points that satisfy the gradient condition
|
|
selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False)
|
|
selected_pts_mask = torch.logical_and(selected_pts_mask,
|
|
torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent)
|
|
|
|
new_xyz = self._xyz[selected_pts_mask]
|
|
new_features_dc = self._features_dc[selected_pts_mask]
|
|
new_features_rest = self._features_rest[selected_pts_mask]
|
|
new_opacities = self._opacity[selected_pts_mask]
|
|
new_scaling = self._scaling[selected_pts_mask]
|
|
new_rotation = self._rotation[selected_pts_mask]
|
|
|
|
new_tmp_radii = self.tmp_radii[selected_pts_mask]
|
|
|
|
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_tmp_radii)
|
|
|
|
def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size, radii):
|
|
grads = self.xyz_gradient_accum / self.denom
|
|
grads[grads.isnan()] = 0.0
|
|
|
|
self.tmp_radii = radii
|
|
self.densify_and_clone(grads, max_grad, extent)
|
|
self.densify_and_split(grads, max_grad, extent)
|
|
|
|
prune_mask = (self.get_opacity < min_opacity).squeeze()
|
|
if max_screen_size:
|
|
big_points_vs = self.max_radii2D > max_screen_size
|
|
big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent
|
|
prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws)
|
|
self.prune_points(prune_mask)
|
|
tmp_radii = self.tmp_radii
|
|
self.tmp_radii = None
|
|
|
|
torch.cuda.empty_cache()
|
|
|
|
def add_densification_stats(self, viewspace_point_tensor, update_filter):
|
|
self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
|
|
self.denom[update_filter] += 1
|