# # 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): 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): self.active_sh_degree = 0 self.max_sh_degree = sh_degree self._xyz = torch.empty(0) 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.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_scaling_with_3D_filter(self): scales = self.get_scaling scales = torch.square(scales) + torch.square(self.filter_3D) scales = torch.sqrt(scales) return scales @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) @property def get_opacity_with_3D_filter(self): opacity = self.opacity_activation(self._opacity) # apply 3D filter scales = self.get_scaling scales_square = torch.square(scales) det1 = scales_square.prod(dim=1) scales_after_square = scales_square + torch.square(self.filter_3D) det2 = scales_after_square.prod(dim=1) coef = torch.sqrt(det1 / det2) return opacity * coef[..., None] def get_covariance(self, scaling_modifier = 1): return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation) @torch.no_grad() def compute_3D_filter(self, cameras): # print("Computing 3D filter") #TODO consider focal length and image width xyz = self.get_xyz distance = torch.ones((xyz.shape[0]), device=xyz.device) * 100000.0 valid_points = torch.zeros((xyz.shape[0]), device=xyz.device, dtype=torch.bool) # we should use the focal length of the highest resolution camera focal_length = 0. for camera in cameras: # transform points to camera space R = torch.tensor(camera.R, device=xyz.device, dtype=torch.float32) T = torch.tensor(camera.T, device=xyz.device, dtype=torch.float32) # R is stored transposed due to 'glm' in CUDA code so we don't neet transopse here xyz_cam = xyz @ R + T[None, :] xyz_to_cam = torch.norm(xyz_cam, dim=1) # project to screen space valid_depth = xyz_cam[:, 2] > 0.2 x, y, z = xyz_cam[:, 0], xyz_cam[:, 1], xyz_cam[:, 2] z = torch.clamp(z, min=0.001) x = x / z * camera.focal_x + camera.image_width / 2.0 y = y / z * camera.focal_y + camera.image_height / 2.0 # in_screen = torch.logical_and(torch.logical_and(x >= 0, x < camera.image_width), torch.logical_and(y >= 0, y < camera.image_height)) # use similar tangent space filtering as in the paper in_screen = torch.logical_and(torch.logical_and(x >= -0.15 * camera.image_width, x <= camera.image_width * 1.15), torch.logical_and(y >= -0.15 * camera.image_height, y <= 1.15 * camera.image_height)) valid = torch.logical_and(valid_depth, in_screen) # distance[valid] = torch.min(distance[valid], xyz_to_cam[valid]) distance[valid] = torch.min(distance[valid], z[valid]) valid_points = torch.logical_or(valid_points, valid) if focal_length < camera.focal_x: focal_length = camera.focal_x distance[~valid_points] = distance[valid_points].max() #TODO remove hard coded value #TODO box to gaussian transform filter_3D = distance / focal_length * (0.2 ** 0.5) self.filter_3D = filter_3D[..., None] 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): self.spatial_lr_scale = spatial_lr_scale fused_point_cloud = torch.tensor(np.asarray(pcd.points)).float().cuda() fused_color = RGB2SH(torch.tensor(np.asarray(pcd.colors)).float().cuda()) features = torch.zeros((fused_color.shape[0], 3, (self.max_sh_degree + 1) ** 2)).float().cuda() features[:, :3, 0 ] = fused_color features[:, 3:, 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._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") 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.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self.xyz_gradient_accum_abs_max = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self.denom = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") 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"} ] 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) 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 def construct_list_of_attributes(self, exclude_filter=False): 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)) if not exclude_filter: l.append('filter_3D') return l 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() filter_3D = self.filter_3D.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, filter_3D), axis=1) elements[:] = list(map(tuple, attributes)) el = PlyElement.describe(elements, 'vertex') PlyData([el]).write(path) def save_fused_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() # fuse opacity and scale current_opacity_with_filter = self.get_opacity_with_3D_filter opacities = inverse_sigmoid(current_opacity_with_filter).detach().cpu().numpy() scale = self.scaling_inverse_activation(self.get_scaling_with_3D_filter).detach().cpu().numpy() rotation = self._rotation.detach().cpu().numpy() dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes(exclude_filter=True)] 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): # reset opacity to by considering 3D filter current_opacity_with_filter = self.get_opacity_with_3D_filter opacities_new = torch.min(current_opacity_with_filter, torch.ones_like(current_opacity_with_filter)*0.01) # apply 3D filter scales = self.get_scaling scales_square = torch.square(scales) det1 = scales_square.prod(dim=1) scales_after_square = scales_square + torch.square(self.filter_3D) det2 = scales_after_square.prod(dim=1) coef = torch.sqrt(det1 / det2) opacities_new = opacities_new / coef[..., None] opacities_new = inverse_sigmoid(opacities_new) 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] filter_3D = np.asarray(plydata.elements[0]["filter_3D"])[..., 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.filter_3D = torch.tensor(filter_3D, dtype=torch.float, device="cuda") 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: old_param = group['params'][0] stored_state = self.optimizer.state.pop(old_param, None) if stored_state is not None: old_exp_avg = stored_state.pop("exp_avg") exp_avg = old_exp_avg[mask].contiguous() del old_exp_avg torch.cuda.empty_cache() old_exp_avg_sq = stored_state.pop("exp_avg_sq") exp_avg_sq = old_exp_avg_sq[mask].contiguous() del old_exp_avg_sq torch.cuda.empty_cache() new_param = nn.Parameter(old_param[mask].contiguous().requires_grad_(True)) group["params"][0] = new_param stored_state["exp_avg"] = exp_avg stored_state["exp_avg_sq"] = exp_avg_sq self.optimizer.state[new_param] = stored_state optimizable_tensors[group["name"]] = new_param else: new_param = nn.Parameter(old_param[mask].contiguous().requires_grad_(True)) group["params"][0] = new_param optimizable_tensors[group["name"]] = new_param del old_param torch.cuda.empty_cache() 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.xyz_gradient_accum_abs = self.xyz_gradient_accum_abs[valid_points_mask] self.xyz_gradient_accum_abs_max = self.xyz_gradient_accum_abs_max[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, copy_original_en=True): optimizable_tensors = {} for group in self.optimizer.param_groups: assert len(group["params"]) == 1 extension_tensor = tensors_dict[group["name"]] old_param = group['params'][0] stored_state = self.optimizer.state.pop(old_param, None) if stored_state is not None: old_exp_avg = stored_state.pop("exp_avg") new_exp_avg = torch.cat((old_exp_avg, torch.zeros_like(extension_tensor)), dim=0) del old_exp_avg torch.cuda.empty_cache() old_exp_avg_sq = stored_state.pop("exp_avg_sq") new_exp_avg_sq = torch.cat((old_exp_avg_sq, torch.zeros_like(extension_tensor)), dim=0) del old_exp_avg_sq torch.cuda.empty_cache() new_param = nn.Parameter(torch.cat((old_param, extension_tensor), dim=0).requires_grad_(True)) group["params"][0] = new_param stored_state["exp_avg"] = new_exp_avg stored_state["exp_avg_sq"] = new_exp_avg_sq self.optimizer.state[new_param] = stored_state optimizable_tensors[group["name"]] = new_param elif not copy_original_en: new_param = nn.Parameter(extension_tensor.requires_grad_(True)) group["params"][0] = new_param optimizable_tensors[group["name"]] = new_param else: new_param = nn.Parameter(torch.cat((old_param, extension_tensor), dim=0).requires_grad_(True)) group["params"][0] = new_param optimizable_tensors[group["name"]] = new_param del old_param torch.cuda.empty_cache() return optimizable_tensors def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, SR_GS_en=False): d = {"xyz": new_xyz, "f_dc": new_features_dc, "f_rest": new_features_rest, "opacity": new_opacities, "scaling" : new_scaling, "rotation" : new_rotation} if SR_GS_en: optimizable_tensors = self.cat_tensors_to_optimizer(d, copy_original_en=False) else: 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"] #TODO Maybe we don't need to reset the value, it's better to use moving average instead of reset the value self.xyz_gradient_accum = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self.xyz_gradient_accum_abs = torch.zeros((self.get_xyz.shape[0], 1), device="cuda") self.xyz_gradient_accum_abs_max = 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, grads_abs, grad_abs_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) padded_grad_abs = torch.zeros((n_init_points), device="cuda") padded_grad_abs[:grads_abs.shape[0]] = grads_abs.squeeze() selected_pts_mask_abs = torch.where(padded_grad_abs >= grad_abs_threshold, True, False) selected_pts_mask = torch.logical_or(selected_pts_mask, selected_pts_mask_abs) 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) self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacity, new_scaling, new_rotation) del stds, means, samples, rots del new_xyz, new_scaling, new_rotation, new_features_dc, new_features_rest, new_opacity torch.cuda.empty_cache() prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool))) del selected_pts_mask, selected_pts_mask_abs, padded_grad, padded_grad_abs self.prune_points(prune_filter) del prune_filter torch.cuda.empty_cache() def densify_and_clone(self, grads, grad_threshold, grads_abs, grad_abs_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_abs = torch.where(torch.norm(grads_abs, dim=-1) >= grad_abs_threshold, True, False) selected_pts_mask = torch.logical_or(selected_pts_mask, selected_pts_mask_abs) 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] self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation) del selected_pts_mask, selected_pts_mask_abs del new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation torch.cuda.empty_cache() def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size): grads = self.xyz_gradient_accum / self.denom grads[grads.isnan()] = 0.0 grads_abs = self.xyz_gradient_accum_abs / self.denom grads_abs[grads_abs.isnan()] = 0.0 ratio = (torch.norm(grads, dim=-1) >= max_grad).float().mean() # Q = torch.quantile(grads_abs.reshape(-1), 1 - ratio) qqq = np.quantile(grads_abs.reshape(-1).cpu().numpy(), 1 - ratio.cpu().numpy()) before = self._xyz.shape[0] # self.densify_and_clone(grads, max_grad, grads_abs, Q, extent) self.densify_and_clone(grads, max_grad, grads_abs, qqq, extent) clone = self._xyz.shape[0] # self.densify_and_split(grads, max_grad, grads_abs, Q, extent) self.densify_and_split(grads, max_grad, grads_abs, qqq, extent) split = self._xyz.shape[0] 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) prune = self._xyz.shape[0] del grads, grads_abs, prune_mask torch.cuda.empty_cache() return clone - before, split - clone, split - prune 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) #TODO maybe use max instead of average self.xyz_gradient_accum_abs[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,2:], dim=-1, keepdim=True) self.xyz_gradient_accum_abs_max[update_filter] = torch.max(self.xyz_gradient_accum_abs_max[update_filter], torch.norm(viewspace_point_tensor.grad[update_filter,2:], dim=-1, keepdim=True)) self.denom[update_filter] += 1 def super_resolving_gaussians(self, factor, rendering=False): device = self._xyz.device num_points = self._xyz.shape[0] xyz_orig = self._xyz.clone().detach() scalings_orig = self.get_scaling.clone().detach() rotations_orig = self.get_rotation.clone().detach() features_dc_orig = self._features_dc.clone().detach() features_rest_orig = self._features_rest.clone().detach() opacity_orig = self._opacity.clone().detach() filter_3D_orig = self.filter_3D.clone().detach() # --- New Gaussians --- # Need modify: xyz, scaling # Keep the same: rotation, features_dc, features_rest, opacity, filter_3D new_xyz = xyz_orig.repeat(factor**3, 1) # shift_value = 1.0 / factor shift_value = 1.0 # Generate the shifts for x, y, and z axis shift_range = np.linspace(-1 + shift_value, 1 - shift_value, factor) # Create all combinations of shifts in 3D space shift_combinations = torch.from_numpy(np.array([[x, y, z] for x in shift_range for y in shift_range for z in shift_range])).to(device) # extended_points = np.einsum('ij,k->ijk', scalings_orig.cpu().numpy(), shift_combinations).reshape(-1, 3) # Calculate the new points # Initialize and empty list to store the extended points extended_points_offset = [] # Multiply each original point by each shift combination for shift_scale in shift_combinations: try: new_shift = scaling_orig * shift_scale except: new_shift = scalings_orig.detach().cpu().numpy() * shift_scale extended_points_offset.append(new_shift) # Convert the list of arrays to a single numpy array extended_points_offset = torch.vstack(extended_points_offset) new_xyz += extended_points_offset new_rotation = rotations_orig.repeat(factor**3, 1) new_features_dc = features_dc_orig.repeat(factor**3, 1, 1) new_features_rest = features_rest_orig.repeat(factor**3, 1, 1) new_opacities = opacity_orig.repeat(factor**3, 1) scale_new = torch.log(scalings_orig / 2) new_scaling = scale_new.repeat(factor**3, 1) print(" === Number of points before densification postfix ", self._xyz.shape[0]) if not rendering: self.densification_postfix(new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation, SR_GS_en=True) else: self._xyz = new_xyz self._rotation = new_rotation self._features_dc = new_features_dc self._features_rest = new_features_rest self._opacity = new_opacities self._scaling = new_scaling print(" === Number of points after densification postfix ", self._xyz.shape[0])