# # 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 import open3d as o3d from utils.system_utils import mkdir_p from plyfile import PlyData, PlyElement from random import randint 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 # from utils.point_utils import addpoint, combine_pointcloud, downsample_point_cloud_open3d, find_indices_in_A from scene.deformation import deform_network from scene.regulation import compute_plane_smoothness class GaussianModel: def setup_functions(self): def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation): L = build_scaling_rotation(scaling_modifier * scaling, rotation) actual_covariance = L @ L.transpose(1, 2) symm = strip_symmetric(actual_covariance) return symm self.scaling_activation = torch.exp self.scaling_inverse_activation = torch.log self.covariance_activation = build_covariance_from_scaling_rotation self.opacity_activation = torch.sigmoid self.inverse_opacity_activation = inverse_sigmoid self.rotation_activation = torch.nn.functional.normalize def __init__(self, sh_degree : int, args): self.active_sh_degree = 0 self.max_sh_degree = sh_degree self._xyz = torch.empty(0) # self._deformation = torch.empty(0) self._deformation = deform_network(args) # self.grid = TriPlaneGrid() self._features_dc = torch.empty(0) self._features_rest = torch.empty(0) self._scaling = torch.empty(0) self._rotation = torch.empty(0) self._opacity = torch.empty(0) self.max_radii2D = torch.empty(0) self.xyz_gradient_accum = torch.empty(0) self.denom = torch.empty(0) self.optimizer = None self.percent_dense = 0 self.spatial_lr_scale = 0 self._deformation_table = torch.empty(0) self.setup_functions() def capture(self): return ( self.active_sh_degree, self._xyz, self._deformation.state_dict(), self._deformation_table, # self.grid, 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._deformation_table, self._deformation, # self.grid, 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, time_line: int): self.spatial_lr_scale = spatial_lr_scale # breakpoint() fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() features[:, :3, 0 ] = fused_color features[:, 3:, 1:] = 0.0 print("Number of points at initialisation : ", fused_point_cloud.shape[0]) dist2 = torch.clamp_min(distCUDA2(torch.from_numpy(np.asarray(pcd.points)).float().cuda()), 0.0000001) scales = torch.log(torch.sqrt(dist2))[...,None].repeat(1, 3) rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda") rots[:, 0] = 1 opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda")) self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True)) self._deformation = self._deformation.to("cuda") # self.grid = self.grid.to("cuda") self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True)) self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True)) self._scaling = nn.Parameter(scales.requires_grad_(True)) self._rotation = nn.Parameter(rots.requires_grad_(True)) self._opacity = nn.Parameter(opacities.requires_grad_(True)) self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") self._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0) def training_setup(self, training_args): self.percent_dense = training_args.percent_dense 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._deformation_accum = torch.zeros((self.get_xyz.shape[0],3),device="cuda") l = [ {'params': [self._xyz], 'lr': training_args.position_lr_init * self.spatial_lr_scale, "name": "xyz"}, {'params': list(self._deformation.get_mlp_parameters()), 'lr': training_args.deformation_lr_init * self.spatial_lr_scale, "name": "deformation"}, {'params': list(self._deformation.get_grid_parameters()), 'lr': training_args.grid_lr_init * self.spatial_lr_scale, "name": "grid"}, {'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"} ] self.optimizer = torch.optim.Adam(l, lr=0.0, eps=1e-15) 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) self.deformation_scheduler_args = get_expon_lr_func(lr_init=training_args.deformation_lr_init*self.spatial_lr_scale, lr_final=training_args.deformation_lr_final*self.spatial_lr_scale, lr_delay_mult=training_args.deformation_lr_delay_mult, max_steps=training_args.position_lr_max_steps) self.grid_scheduler_args = get_expon_lr_func(lr_init=training_args.grid_lr_init*self.spatial_lr_scale, lr_final=training_args.grid_lr_final*self.spatial_lr_scale, lr_delay_mult=training_args.deformation_lr_delay_mult, max_steps=training_args.position_lr_max_steps) def update_learning_rate(self, iteration): ''' 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 if "grid" in param_group["name"]: lr = self.grid_scheduler_args(iteration) param_group['lr'] = lr # return lr elif param_group["name"] == "deformation": lr = self.deformation_scheduler_args(iteration) param_group['lr'] = lr # return lr def construct_list_of_attributes(self): l = ['x', 'y', 'z', 'nx', 'ny', 'nz'] # All channels except the 3 DC for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]): l.append('f_dc_{}'.format(i)) for i in range(self._features_rest.shape[1]*self._features_rest.shape[2]): l.append('f_rest_{}'.format(i)) l.append('opacity') for i in range(self._scaling.shape[1]): l.append('scale_{}'.format(i)) for i in range(self._rotation.shape[1]): l.append('rot_{}'.format(i)) return l def compute_deformation(self,time): deform = self._deformation[:,:,:time].sum(dim=-1) xyz = self._xyz + deform return xyz # def save_ply_dynamic(path): # for time in range(self._deformation.shape(-1)): # xyz = self.compute_deformation(time) def load_model(self, path): print("loading model from exists{}".format(path)) weight_dict = torch.load(os.path.join(path,"deformation.pth"),map_location="cuda") self._deformation.load_state_dict(weight_dict) self._deformation = self._deformation.to("cuda") self._deformation_table = torch.gt(torch.ones((self.get_xyz.shape[0]),device="cuda"),0) self._deformation_accum = torch.zeros((self.get_xyz.shape[0],3),device="cuda") if os.path.exists(os.path.join(path, "deformation_table.pth")): self._deformation_table = torch.load(os.path.join(path, "deformation_table.pth"),map_location="cuda") if os.path.exists(os.path.join(path, "deformation_accum.pth")): self._deformation_accum = torch.load(os.path.join(path, "deformation_accum.pth"),map_location="cuda") self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda") # print(self._deformation.deformation_net.grid.) def save_deformation(self, path): torch.save(self._deformation.state_dict(),os.path.join(path, "deformation.pth")) torch.save(self._deformation_table,os.path.join(path, "deformation_table.pth")) torch.save(self._deformation_accum,os.path.join(path, "deformation_accum.pth")) def save_ply(self, path): mkdir_p(os.path.dirname(path)) xyz = self._xyz.detach().cpu().numpy() normals = np.zeros_like(xyz) 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) 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): 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") self._opacity = optimizable_tensors["opacity"] def load_ply(self, path): 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): 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) stored_state["exp_avg_sq"] = torch.zeros_like(tensor) 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): optimizable_tensors = {} for group in self.optimizer.param_groups: if len(group["params"]) > 1: continue 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._deformation_accum = self._deformation_accum[valid_points_mask] self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] self._deformation_table = self._deformation_table[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): optimizable_tensors = {} for group in self.optimizer.param_groups: if len(group["params"])>1:continue 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_deformation_table): d = {"xyz": new_xyz, "f_dc": new_features_dc, "f_rest": new_features_rest, "opacity": new_opacities, "scaling" : new_scaling, "rotation" : new_rotation, # "deformation": new_deformation } 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._deformation = optimizable_tensors["deformation"] self._deformation_table = torch.cat([self._deformation_table,new_deformation_table],-1) self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self._deformation_accum = torch.zeros((self.get_xyz.shape[0], 3), 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) # breakpoint() selected_pts_mask = torch.logical_and(selected_pts_mask, torch.max(self.get_scaling, dim=1).values > self.percent_dense*scene_extent) if not selected_pts_mask.any(): return 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_deformation_table = self._deformation_table[selected_pts_mask].repeat(N) self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation, new_deformation_table) 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, density_threshold=20, displacement_scale=20, model_path=None, iteration=None, stage=None): grads_accum_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False) # 主动增加稀疏点云 # if not hasattr(self,"voxel_size"): # self.voxel_size = 8 # if not hasattr(self,"density_threshold"): # self.density_threshold = density_threshold # if not hasattr(self,"displacement_scale"): # self.displacement_scale = displacement_scale # point_cloud = self.get_xyz.detach().cpu() # sparse_point_mask = self.downsample_point(point_cloud) # _, low_density_points, new_points, low_density_index = addpoint(point_cloud[sparse_point_mask],density_threshold=self.density_threshold,displacement_scale=self.displacement_scale,iter_pass=0) # sparse_point_mask = sparse_point_mask.to(grads_accum_mask) # low_density_index = low_density_index.to(grads_accum_mask) # if new_points.shape[0] < 100 : # self.density_threshold /= 2 # self.displacement_scale /= 2 # print("reduce diplacement_scale to: ",self.displacement_scale) # global_mask = torch.zeros((point_cloud.shape[0]), dtype=torch.bool).to(grads_accum_mask) # global_mask[sparse_point_mask] = low_density_index # selected_pts_mask_grow = torch.logical_and(global_mask, grads_accum_mask) # print("降采样点云:",sparse_point_mask.sum(),"选中的稀疏点云：",global_mask.sum(),"梯度累计点云：",grads_accum_mask.sum(),"选中增长点云：",selected_pts_mask_grow.sum()) # Extract points that satisfy the gradient condition selected_pts_mask = torch.logical_and(grads_accum_mask, torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent) # breakpoint() new_xyz = self._xyz[selected_pts_mask] # - 0.001 * self._xyz.grad[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_deformation_table = self._deformation_table[selected_pts_mask] # if opt.add_point: # selected_xyz, grow_xyz = self.add_point_by_mask(selected_pts_mask_grow.to(self.get_xyz.device), self.displacement_scale) self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_deformation_table) # print("被动增加点云：",selected_xyz.shape[0]) # print("主动增加点云：",selected_pts_mask.sum()) # if model_path is not None and iteration is not None: # point = combine_pointcloud(self.get_xyz.detach().cpu().numpy(), new_xyz.detach().cpu().numpy(), selected_xyz.detach().cpu().numpy()) # write_path = os.path.join(model_path,"add_point_cloud") # os.makedirs(write_path,exist_ok=True) # o3d.io.write_point_cloud(os.path.join(write_path,f"iteration_{stage}{iteration}.ply"),point) # print("write output.") @property def get_aabb(self): return self._deformation.get_aabb def get_displayment(self,selected_point, point, perturb): xyz_max, xyz_min = self.get_aabb displacements = torch.randn(selected_point.shape[0], 3).to(selected_point) * perturb final_point = selected_point + displacements mask_a = final_pointxyz_min mask_c = mask_a & mask_b mask_d = mask_c.all(dim=1) final_point = final_point[mask_d] # while (mask_d.sum()/final_point.shape[0])<0.5: # perturb/=2 # displacements = torch.randn(selected_point.shape[0], 3).to(selected_point) * perturb # final_point = selected_point + displacements # mask_a = final_pointxyz_min # mask_c = mask_a & mask_b # mask_d = mask_c.all(dim=1) # final_point = final_point[mask_d] return final_point, mask_d def add_point_by_mask(self, selected_pts_mask, perturb=0): selected_xyz = self._xyz[selected_pts_mask] new_xyz, mask = self.get_displayment(selected_xyz, self.get_xyz.detach(),perturb) # displacements = torch.randn(selected_xyz.shape[0], 3).to(self._xyz) * perturb # new_xyz = selected_xyz + displacements # - 0.001 * self._xyz.grad[selected_pts_mask] new_features_dc = self._features_dc[selected_pts_mask][mask] new_features_rest = self._features_rest[selected_pts_mask][mask] new_opacities = self._opacity[selected_pts_mask][mask] new_scaling = self._scaling[selected_pts_mask][mask] new_rotation = self._rotation[selected_pts_mask][mask] new_deformation_table = self._deformation_table[selected_pts_mask][mask] self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, new_deformation_table) return selected_xyz, new_xyz def downsample_point(self, point_cloud): if not hasattr(self,"voxel_size"): self.voxel_size = 8 point_downsample = point_cloud flag = False while point_downsample.shape[0]>1000: if flag: self.voxel_size+=8 point_downsample = downsample_point_cloud_open3d(point_cloud,voxel_size=self.voxel_size) flag = True print("point size:",point_downsample.shape[0]) # downsampled_point_mask = torch.eq(point_downsample.view(1,-1,3), point_cloud.view(-1,1,3)).all(dim=1) downsampled_point_index = find_indices_in_A(point_cloud, point_downsample) downsampled_point_mask = torch.zeros((point_cloud.shape[0]), dtype=torch.bool).to(point_downsample.device) downsampled_point_mask[downsampled_point_index]=True return downsampled_point_mask def grow(self, density_threshold=20, displacement_scale=20, model_path=None, iteration=None, stage=None): if not hasattr(self,"voxel_size"): self.voxel_size = 8 if not hasattr(self,"density_threshold"): self.density_threshold = density_threshold if not hasattr(self,"displacement_scale"): self.displacement_scale = displacement_scale flag = False point_cloud = self.get_xyz.detach().cpu() point_downsample = point_cloud.detach() downsampled_point_index = self.downsample_point(point_downsample) _, low_density_points, new_points, low_density_index = addpoint(point_cloud[downsampled_point_index],density_threshold=self.density_threshold,displacement_scale=self.displacement_scale,iter_pass=0) if new_points.shape[0] < 100 : self.density_threshold /= 2 self.displacement_scale /= 2 print("reduce diplacement_scale to: ",self.displacement_scale) elif new_points.shape[0] == 0: print("no point added") return global_mask = torch.zeros((point_cloud.shape[0]), dtype=torch.bool) global_mask[downsampled_point_index] = low_density_index global_mask selected_xyz, new_xyz = self.add_point_by_mask(global_mask.to(self.get_xyz.device), self.displacement_scale) print("point growing,add point num:",global_mask.sum()) if model_path is not None and iteration is not None: point = combine_pointcloud(point_cloud, selected_xyz.detach().cpu().numpy(), new_xyz.detach().cpu().numpy()) write_path = os.path.join(model_path,"add_point_cloud") os.makedirs(write_path,exist_ok=True) o3d.io.write_point_cloud(os.path.join(write_path,f"iteration_{stage}{iteration}.ply"),point) return def prune(self, max_grad, min_opacity, extent, max_screen_size): 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(prune_mask, big_points_vs) prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws) self.prune_points(prune_mask) torch.cuda.empty_cache() def densify(self, max_grad, min_opacity, extent, max_screen_size, density_threshold, displacement_scale, model_path=None, iteration=None, stage=None): grads = self.xyz_gradient_accum / self.denom grads[grads.isnan()] = 0.0 self.densify_and_clone(grads, max_grad, extent, density_threshold, displacement_scale, model_path, iteration, stage) self.densify_and_split(grads, max_grad, extent) def standard_constaint(self): means3D = self._xyz.detach() scales = self._scaling.detach() rotations = self._rotation.detach() opacity = self._opacity.detach() time = torch.tensor(0).to("cuda").repeat(means3D.shape[0],1) means3D_deform, scales_deform, rotations_deform, _ = self._deformation(means3D, scales, rotations, opacity, time) position_error = (means3D_deform - means3D)**2 rotation_error = (rotations_deform - rotations)**2 scaling_erorr = (scales_deform - scales)**2 return position_error.mean() + rotation_error.mean() + scaling_erorr.mean() def add_densification_stats(self, viewspace_point_tensor, update_filter): self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor[update_filter,:2], dim=-1, keepdim=True) self.denom[update_filter] += 1 @torch.no_grad() def update_deformation_table(self,threshold): # print("origin deformation point nums:",self._deformation_table.sum()) self._deformation_table = torch.gt(self._deformation_accum.max(dim=-1).values/100,threshold) def print_deformation_weight_grad(self): for name, weight in self._deformation.named_parameters(): if weight.requires_grad: if weight.grad is None: print(name," :",weight.grad) else: if weight.grad.mean() != 0: print(name," :",weight.grad.mean(), weight.grad.min(), weight.grad.max()) print("-"*50) def _plane_regulation(self): multi_res_grids = self._deformation.deformation_net.grid.grids total = 0 # model.grids is 6 x [1, rank * F_dim, reso, reso] for grids in multi_res_grids: if len(grids) == 3: time_grids = [] else: time_grids = [0,1,3] for grid_id in time_grids: total += compute_plane_smoothness(grids[grid_id]) return total def _time_regulation(self): multi_res_grids = self._deformation.deformation_net.grid.grids total = 0 # model.grids is 6 x [1, rank * F_dim, reso, reso] for grids in multi_res_grids: if len(grids) == 3: time_grids = [] else: time_grids =[2, 4, 5] for grid_id in time_grids: total += compute_plane_smoothness(grids[grid_id]) return total def _l1_regulation(self): # model.grids is 6 x [1, rank * F_dim, reso, reso] multi_res_grids = self._deformation.deformation_net.grid.grids total = 0.0 for grids in multi_res_grids: if len(grids) == 3: continue else: # These are the spatiotemporal grids spatiotemporal_grids = [2, 4, 5] for grid_id in spatiotemporal_grids: total += torch.abs(1 - grids[grid_id]).mean() return total def compute_regulation(self, time_smoothness_weight, l1_time_planes_weight, plane_tv_weight): return plane_tv_weight * self._plane_regulation() + time_smoothness_weight * self._time_regulation() + l1_time_planes_weight * self._l1_regulation()