# # 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 torch import numpy as np from utils.general_utils import inverse_sigmoid, get_expon_lr_func, build_rotation from torch import nn import os from utils.system_utils import mkdir_p from plyfile import PlyData, PlyElement from utils.sh_utils import RGB2SH from simple_knn._C import distCUDA2 from utils.graphics_utils import BasicPointCloud from utils.general_utils import strip_symmetric, build_scaling_rotation class GaussianModel: def setup_functions(self): """ 定义和初始化处理高斯体模型参数的 激活函数 """ # 定义 从尺度、旋转构建3D高斯的 协方差矩阵 的函数 def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): L = build_scaling_rotation(scaling_modifier * scaling, rotation) # 从尺度、尺度的缩放、旋转得到L矩阵 actual_covariance = L @ L.transpose(1, 2) # 计算实际的协方差矩阵 symm = strip_symmetric(actual_covariance) # 提取对称部分 return symm # 初始化一些激活函数 self.scaling_activation = torch.exp # 用exp函数，确保尺度参数非负 self.scaling_inverse_activation = torch.log # 尺度的逆激活函数，用于梯度回传 self.covariance_activation = build_covariance_from_scaling_rotation # 协方差矩阵的激活函数 self.opacity_activation = torch.sigmoid # 用sigmoid函数，确保不透明度在0到1之间 self.inverse_opacity_activation = inverse_sigmoid # 不透明度的逆激活函数 self.rotation_activation = torch.nn.functional.normalize # 用于标准化旋转参数的函数 def __init__(self, sh_degree : int): """ 初始化3D高斯模型的参数 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 """ self.active_sh_degree = 0 # 当前激活的球谐阶数，初始为0 self.max_sh_degree = sh_degree # 允许的最大球谐阶数j # 初始化3D高斯模型的各项参数 self._xyz = torch.empty(0) # 3D高斯的 中心位置（均值） self._features_dc = torch.empty(0) # 第一个球谐系数，用于表示基础颜色 self._features_rest = torch.empty(0) # 其余球谐系数，用于表示颜色的细节和变化 self._scaling = torch.empty(0) # 3D高斯的尺度，控制高斯的形状 self._rotation = torch.empty(0) # 3D高斯的旋转（一系列四元数） self._opacity = torch.empty(0) # 3D高斯的不透明度（sigmoid前的），控制可见性 self.max_radii2D = torch.empty(0) # 在2D投影中，每个高斯的最大半径 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 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） 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） self.percent_dense = 0 # 控制Gaussian密集程度的超参数 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 # 调用 setup_functions，初始化处理高斯体模型参数的 激活函数 self.setup_functions() def capture(self): return ( self.active_sh_degree, self._xyz, self._features_dc, self._features_rest, self._scaling, self._rotation, self._opacity, self.max_radii2D, self.xyz_gradient_accum, self.denom, self.optimizer.state_dict(), self.spatial_lr_scale, ) def restore(self, model_args, training_args): (self.active_sh_degree, self._xyz, self._features_dc, self._features_rest, self._scaling, self._rotation, self._opacity, self.max_radii2D, xyz_gradient_accum, denom, opt_dict, self.spatial_lr_scale) = model_args self.training_setup(training_args) self.xyz_gradient_accum = xyz_gradient_accum self.denom = denom self.optimizer.load_state_dict(opt_dict) @property def get_scaling(self): return self.scaling_activation(self._scaling) @property def get_rotation(self): return self.rotation_activation(self._rotation) @property def get_xyz(self): return self._xyz @property def get_features(self): features_dc = self._features_dc features_rest = self._features_rest return torch.cat((features_dc, features_rest), dim=1) @property def get_opacity(self): return self.opacity_activation(self._opacity) def get_covariance(self, scaling_modifier = 1): return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation) def oneupSHdegree(self): if self.active_sh_degree < self.max_sh_degree: self.active_sh_degree += 1 def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float): """ 从稀疏点云数据pcd 初始化模型参数 pcd: 稀疏点云，包含点的位置和颜色 spatial_lr_scale: 空间学习率缩放因子，影响 位置坐标参数的学习率 """ # 根据scene.Scene.__init__ 以及 scene.dataset_readers.SceneInfo.nerf_normalization，即scene.dataset_readers.getNerfppNorm的代码， # 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。 self.spatial_lr_scale = spatial_lr_scale # 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 fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() # 稀疏点云的3D坐标，大小为(P, 3) fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) # 球谐的直流分量，大小为(P, 3)， # 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 # 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 features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() # (P, 3, 16) 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 features[:, 3:, 1:] = 0.0 # 其余球谐系数初始化为0 # 打印初始点的数量 print("Number of points at initialisation : ", fused_point_cloud.shape[0]) # 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 # 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 dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) # (P,) # 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)。 # 因dist2其实是距离的平方，所以这里要开根号 # repeat(1, 3) 标明三个方向上scale的初始值是相等的 scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) # (P, 3) # 初始化每个点的旋转参数为单位四元数（无旋转） rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") # (P, 4) rots[:, 0] = 1 # 四元数的实部为1，表示无旋转 # 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))。 # 不透明度存储的时候要取其经历sigmoid前的值，inverse_sigmoid(0.1) = -2.197 opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) # (P, 1) # 将以上计算的参数设置为模型的可训练参数 self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) # 高斯椭球体中心位置坐标，(N, 3) 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) 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) self._scaling = nn.Parameter(scales.requires_grad_(True)) # 尺度(N, 3) self._rotation = nn.Parameter(rots.requires_grad_(True)) # 旋转四元数(N, 4) self._opacity = nn.Parameter(opacities.requires_grad_(True)) # 不透明度（经过sigmoid之前），(N, 1) 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,) def training_setup(self, training_args): """ 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 :param training_args: 包含训练相关参数的对象 """ # 设置在训练过程中，用于密集化处理的3D高斯点的比例 # 控制Gaussian的密度，在`densify_and_clone`中被使用 self.percent_dense = training_args.percent_dense # 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 self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") # 坐标的累积梯度 self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") # 意义不明 # 配置各参数的优化器，包括指定参数、学习率和参数名称 l = [ {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, {'params': [self._features_dc], 'lr': training_args.feature_lr, "name": "f_dc"}, {'params': [self._features_rest], 'lr': training_args.feature_lr / 20.0, "name": "f_rest"}, {'params': [self._opacity], 'lr': training_args.opacity_lr, "name": "opacity"}, {'params': [self._scaling], 'lr': training_args.scaling_lr, "name": "scaling"}, {'params': [self._rotation], 'lr': training_args.rotation_lr, "name": "rotation"} ] # 创建优化器，这里使用Adam优化器 self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) # 创建学习率调度器，用于对中心点位置的学习率进行调整 self.xyz_scheduler_args = get_expon_lr_func(lr_init=training_args.position_lr_init*self.spatial_lr_scale, lr_final=training_args.position_lr_final*self.spatial_lr_scale, lr_delay_mult=training_args.position_lr_delay_mult, max_steps=training_args.position_lr_max_steps) def update_learning_rate(self, iteration): # 更新Gaussian坐标的学习率 ''' Learning rate scheduling per step ''' for param_group in self.optimizer.param_groups: if param_group["name"] == "xyz": lr = self.xyz_scheduler_args(iteration) param_group['lr'] = lr return lr # 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' def construct_list_of_attributes(self): # 构建ply文件的键列表 l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] # 不知道nx，ny,nz的用处 # All channels except the 3 DC for i in range(self._features_dc.shape[1] * self._features_dc.shape[2]): # self._features_dc: (N, 3, 1) l.append('f_dc_{}'.format(i)) 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) l.append('f_rest_{}'.format(i)) l.append('opacity') for i in range(self._scaling.shape[1]): # shape[1]: 3 l.append('scale_{}'.format(i)) for i in range(self._rotation.shape[1]): # shape[1]: 4 l.append('rot_{}'.format(i)) return l def save_ply(self, path): mkdir_p(os.path.dirname(path)) xyz = self._xyz.detach().cpu().numpy() normals = np.zeros_like(xyz) # # nx, ny, nz；全是0，不知何用 f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() f_rest = self._features_rest.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy() opacities = self._opacity.detach().cpu().numpy() scale = self._scaling.detach().cpu().numpy() rotation = self._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) # 所有要保存的值合并成一个大数组 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 reset_opacity(self): # get_opacity返回了经过exp的不透明度，是真的不透明度 # 这句话让所有不透明度都不能超过0.01 opacities_new = inverse_sigmoid(torch.min(self.get_opacity, torch.ones_like(self.get_opacity) * 0.01)) optimizable_tensors = self.replace_tensor_to_optimizer(opacities_new, "opacity") # 更新优化器中的不透明度 self._opacity = optimizable_tensors["opacity"] def load_ply(self, path): # 读取ply文件并把数据转换成torch.nn.Parameter等待优化 plydata = PlyData.read(path) xyz = np.stack((np.asarray(plydata.elements[0]["x"]), np.asarray(plydata.elements[0]["y"]), np.asarray(plydata.elements[0]["z"])), axis=1) opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis] features_dc = np.zeros((xyz.shape[0], 3, 1)) features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"]) features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"]) features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"]) extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")] extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1])) assert len(extra_f_names)==3*(self.max_sh_degree + 1) ** 2 - 3 features_extra = np.zeros((xyz.shape[0], len(extra_f_names))) for idx, attr_name in enumerate(extra_f_names): features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name]) # Reshape (P,F*SH_coeffs) to (P, F, SH_coeffs except DC) features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1)) scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")] scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1])) scales = np.zeros((xyz.shape[0], len(scale_names))) for idx, attr_name in enumerate(scale_names): scales[:, idx] = np.asarray(plydata.elements[0][attr_name]) rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")] rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1])) rots = np.zeros((xyz.shape[0], len(rot_names))) for idx, attr_name in enumerate(rot_names): rots[:, idx] = np.asarray(plydata.elements[0][attr_name]) self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True)) self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True)) 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)) 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