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https://github.com/graphdeco-inria/gaussian-splatting
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496 lines
27 KiB
Python
496 lines
27 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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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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class GaussianModel:
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def setup_functions(self):
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"""
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定义和初始化处理高斯体模型参数的 激活函数
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"""
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# 定义 从尺度、旋转构建3D高斯的 协方差矩阵 的函数
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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) # 从尺度、尺度的缩放、旋转得到L矩阵
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actual_covariance = L @ L.transpose(1, 2) # 计算实际的协方差矩阵
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symm = strip_symmetric(actual_covariance) # 提取对称部分
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return symm
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# 初始化一些激活函数
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self.scaling_activation = torch.exp # 用exp函数，确保尺度参数非负
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self.scaling_inverse_activation = torch.log # 尺度的逆激活函数，用于梯度回传
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self.covariance_activation = build_covariance_from_scaling_rotation # 协方差矩阵的激活函数
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self.opacity_activation = torch.sigmoid # 用sigmoid函数，确保不透明度在0到1之间
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self.inverse_opacity_activation = inverse_sigmoid # 不透明度的逆激活函数
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self.rotation_activation = torch.nn.functional.normalize # 用于标准化旋转参数的函数
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def __init__(self, sh_degree : int):
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"""
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初始化3D高斯模型的参数
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sh_degree: 设定的 球谐函数的最大阶数，默认为3，用于控制颜色表示的复杂度
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"""
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self.active_sh_degree = 0 # 当前激活的球谐阶数，初始为0
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self.max_sh_degree = sh_degree # 允许的最大球谐阶数j
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# 初始化3D高斯模型的各项参数
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self._xyz = torch.empty(0) # 3D高斯的 中心位置（均值）
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self._features_dc = torch.empty(0) # 第一个球谐系数，用于表示基础颜色
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self._features_rest = torch.empty(0) # 其余球谐系数，用于表示颜色的细节和变化
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self._scaling = torch.empty(0) # 3D高斯的尺度，控制高斯的形状
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self._rotation = torch.empty(0) # 3D高斯的旋转（一系列四元数）
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self._opacity = torch.empty(0) # 3D高斯的不透明度（sigmoid前的），控制可见性
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self.max_radii2D = torch.empty(0) # 在2D投影中，每个高斯的最大半径
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self.xyz_gradient_accum = torch.empty(0) # 累积3D高斯中心位置的梯度，当它太大的时候要对Gaussian进行分裂，小时代表under要复制
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self.denom = torch.empty(0) # 与累积梯度配合使用，表示统计了多少次累积梯度，算平均梯度时除掉这个（denom = denominator，分母）
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self.optimizer = None # 优化器，用于调整上述参数以改进模型（论文中采用Adam，见附录B Algorithm 1的伪代码）
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self.percent_dense = 0 # 控制Gaussian密集程度的超参数
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self.spatial_lr_scale = 0 # 位置坐标的学习率要乘上这个，抵消在不同尺度下应用同一个学习率带来的问题
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# 调用 setup_functions，初始化处理高斯体模型参数的 激活函数
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self.setup_functions()
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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_opacity(self):
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return self.opacity_activation(self._opacity)
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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, pcd : BasicPointCloud, spatial_lr_scale : float):
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"""
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从稀疏点云数据pcd 初始化模型参数
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pcd: 稀疏点云，包含点的位置和颜色
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spatial_lr_scale: 空间学习率缩放因子，影响 位置坐标参数的学习率
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"""
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# 根据scene.Scene.__init__ 以及 scene.dataset_readers.SceneInfo.nerf_normalization，即scene.dataset_readers.getNerfppNorm的代码，
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# 这个值似乎是训练相机中离它们的坐标平均值（即中心）最远距离的1.1倍，根据命名推断应该与学习率有关，防止固定的学习率适配不同尺度的场景时出现问题。
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self.spatial_lr_scale = spatial_lr_scale
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# 将点云的 位置 和 颜色 数据从numpy数组转换为PyTorch张量，并传送到CUDA设备上
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fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() # 稀疏点云的3D坐标，大小为(P, 3)
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fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) # 球谐的直流分量，大小为(P, 3)，
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# RGB2SH(x) = (x - 0.5) / 0.28209479177387814看样子pcd.colors的原始范围应该是0到1。0.28209479177387814是1 / (2*sqrt(pi))，是直流分量Y(l=0,m=0)的值
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# 初始化存储 球谐系数 的张量，RGB三通道球谐的所有系数，每个通道有(max_sh_degree + 1) ** 2个球谐系数
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features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() # (P, 3, 16)
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features[:, :3, 0 ] = fused_color # 将RGB转换后的球谐系数C0项的系数(直流分量)存入每个3D点的直流分量球谐系数中
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features[:, 3:, 1:] = 0.0 # 其余球谐系数初始化为0
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# 打印初始点的数量
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print("Number of points at initialisation : ", fused_point_cloud.shape[0])
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# 计算点云中每个点到其最近的k个点的平均距离的 平方，用于确定高斯的尺度参数scale，且scale（的平方）不能低于1e-7
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# distCUDA2由 submodules/simple-knn/simple_knn.cu 的 SimpleKNN::knn 函数实现，KNN意思是K-Nearest Neighbor，即求每一点最近的K个点
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dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) # (P,)
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# 因为scale的激活函数是exp，所以这里存的也不是真的scale，而是ln(scale)。
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# 因dist2其实是距离的平方，所以这里要开根号
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# repeat(1, 3) 标明三个方向上scale的初始值是相等的
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scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) # (P, 3)
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# 初始化每个点的旋转参数为单位四元数（无旋转）
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rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") # (P, 4)
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rots[:, 0] = 1 # 四元数的实部为1，表示无旋转
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# 初始化每个点的不透明度在sigmoid前的值为0.1，inverse_sigmoid是sigmoid的反函数，等于ln(x / (1 - x))。
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# 不透明度存储的时候要取其经历sigmoid前的值，inverse_sigmoid(0.1) = -2.197
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opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) # (P, 1)
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# 将以上计算的参数设置为模型的可训练参数
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self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) # 高斯椭球体中心位置坐标，(N, 3)
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self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) # RGB三个通道球谐系数的直流分量（C0项），(N, 3, 1)
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self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) # RGB三个通道球谐系数的高阶分量，(N, 3, (最大球谐阶数 + 1)² - 1)
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self._scaling = nn.Parameter(scales.requires_grad_(True)) # 尺度(N, 3)
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self._rotation = nn.Parameter(rots.requires_grad_(True)) # 旋转四元数(N, 4)
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self._opacity = nn.Parameter(opacities.requires_grad_(True)) # 不透明度（经过sigmoid之前），(N, 1)
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self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") # 存储2D投影的最大半径，初始化为0，大小为(N,)
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def training_setup(self, training_args):
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"""
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设置训练参数，包括初始化用于累积梯度的变量，配置优化器，以及创建学习率调度器
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:param training_args: 包含训练相关参数的对象
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"""
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# 设置在训练过程中，用于密集化处理的3D高斯点的比例
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# 控制Gaussian的密度，在`densify_and_clone`中被使用
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self.percent_dense = training_args.percent_dense
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# 初始化用于累积3D高斯中心点位置梯度的张量，用于之后判断是否需要对3D高斯进行克隆或切分
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self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") # 坐标的累积梯度
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self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") # 意义不明
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# 配置各参数的优化器，包括指定参数、学习率和参数名称
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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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# 创建优化器，这里使用Adam优化器
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self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15)
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# 创建学习率调度器，用于对中心点位置的学习率进行调整
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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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def update_learning_rate(self, iteration):
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# 更新Gaussian坐标的学习率
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''' Learning rate scheduling per step '''
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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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# 模型被保存到了/point_cloud/iteration_xxx/point_cloud.ply文件中，使用PlyData.read()读取，其第一个属性，即vertex的信息为：x', 'y', 'z', 'nx', 'ny', 'nz', 3个'f_dc_x', 45个'f_rest_xx', 'opacity', 3个'scale_x', 4个'rot_x'
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def construct_list_of_attributes(self):
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# 构建ply文件的键列表
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l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] # 不知道nx，ny,nz的用处
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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]): # self._features_dc: (N, 3, 1)
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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]): # self._features_rest: (N, 3, (最大球谐阶数 + 1)² - 1)
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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]): # shape[1]: 3
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l.append('scale_{}'.format(i))
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for i in range(self._rotation.shape[1]): # shape[1]: 4
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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) # # nx, ny, nz；全是0，不知何用
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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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# 所有要保存的值合并成一个大数组
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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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# get_opacity返回了经过exp的不透明度，是真的不透明度
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# 这句话让所有不透明度都不能超过0.01
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opacities_new = inverse_sigmoid(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") # 更新优化器中的不透明度
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self._opacity = optimizable_tensors["opacity"]
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def load_ply(self, path):
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# 读取ply文件并把数据转换成torch.nn.Parameter等待优化
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plydata = PlyData.read(path)
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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):
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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):
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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))
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self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
|
||
self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
|
||
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):
|
||
# 看样子是把优化器保存的某个名为`name`的参数的值强行替换为`tensor`
|
||
# 这里面需要注意的是修改Adam优化器的状态变量：动量（momentum）和平方动量（second-order momentum）
|
||
optimizable_tensors = {}
|
||
for group in self.optimizer.param_groups:
|
||
if group["name"] == name:
|
||
stored_state = self.optimizer.state.get(group['params'][0], None)
|
||
stored_state["exp_avg"] = torch.zeros_like(tensor) # 把动量清零
|
||
stored_state["exp_avg_sq"] = torch.zeros_like(tensor) # 把平方动量清零
|
||
|
||
del self.optimizer.state[group['params'][0]]
|
||
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):
|
||
# 根据`mask`裁剪一部分参数及其动量和二阶动量
|
||
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):
|
||
# 删除Gaussian并移除对应的所有属性
|
||
valid_points_mask = ~mask
|
||
optimizable_tensors = self._prune_optimizer(valid_points_mask)
|
||
|
||
# 重置各个参数
|
||
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]
|
||
|
||
def cat_tensors_to_optimizer(self, tensors_dict):
|
||
# 把新的张量字典添加到优化器
|
||
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):
|
||
# 新增Gaussian，把新属性添加到优化器中
|
||
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.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)
|
||
|
||
'''
|
||
被分裂的Gaussians满足两个条件：
|
||
1. （平均）梯度过大；
|
||
2. 在某个方向的最大缩放大于一个阈值。
|
||
参照论文5.2节“On the other hand...”一段，大Gaussian被分裂成两个小Gaussians，
|
||
其放缩被除以φ=1.6，且位置是以原先的大Gaussian作为概率密度函数进行采样的。
|
||
'''
|
||
|
||
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)
|
||
# 算出随机采样出来的新坐标。bmm: batch matrix-matrix product
|
||
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)
|
||
|
||
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation)
|
||
|
||
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)
|
||
# 提取出大于阈值`grad_threshold`且缩放参数较小（小于self.percent_dense * scene_extent）的Gaussians，在下面进行克隆
|
||
|
||
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]
|
||
|
||
self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)
|
||
|
||
def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
|
||
"""
|
||
max_grad 决定是否应基于 2D 位置梯度对点进行densification的限制，默认为0.0002
|
||
min_opacity 0.005
|
||
extent
|
||
max_screen_size 初始为None，3000代后，即后续重置不透明度，则为20
|
||
"""
|
||
grads = self.xyz_gradient_accum / self.denom # 计算平均梯度
|
||
grads[grads.isnan()] = 0.0
|
||
|
||
self.densify_and_clone(grads, max_grad, extent) # 通过克隆增加密度
|
||
self.densify_and_split(grads, max_grad, extent) # 通过分裂增加密度
|
||
|
||
# 接下来移除一些Gaussians，它们满足下列要求中的一个：
|
||
# 1. 接近透明（不透明度小于min_opacity）
|
||
# 2. 在某个相机视野里出现过的最大2D半径大于屏幕（像平面）大小
|
||
# 3. 在某个方向的最大缩放大于0.1 * extent（也就是说很长的长条形也是会被移除的）
|
||
prune_mask = (self.get_opacity < min_opacity).squeeze()
|
||
if max_screen_size:
|
||
big_points_vs = self.max_radii2D > max_screen_size # vs = view space?
|
||
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) # ws = world space?
|
||
self.prune_points(prune_mask)
|
||
|
||
torch.cuda.empty_cache()
|
||
|
||
def add_densification_stats(self, viewspace_point_tensor, update_filter):
|
||
# 统计坐标的累积梯度和均值的分母（即迭代步数？）
|
||
self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True)
|
||
self.denom[update_filter] += 1 |