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| import os | |
| import math | |
| import numpy as np | |
| from typing import NamedTuple | |
| from plyfile import PlyData, PlyElement | |
| import torch | |
| from torch import nn | |
| from diff_gaussian_rasterization import ( | |
| GaussianRasterizationSettings, | |
| GaussianRasterizer, | |
| ) | |
| from simple_knn._C import distCUDA2 | |
| from sh_utils import eval_sh, SH2RGB, RGB2SH | |
| from mesh import Mesh | |
| from mesh_utils import decimate_mesh, clean_mesh | |
| import kiui | |
| def inverse_sigmoid(x): | |
| return torch.log(x/(1-x)) | |
| def get_expon_lr_func( | |
| lr_init, lr_final, lr_delay_steps=0, lr_delay_mult=1.0, max_steps=1000000 | |
| ): | |
| def helper(step): | |
| if lr_init == lr_final: | |
| # constant lr, ignore other params | |
| return lr_init | |
| if step < 0 or (lr_init == 0.0 and lr_final == 0.0): | |
| # Disable this parameter | |
| return 0.0 | |
| if lr_delay_steps > 0: | |
| # A kind of reverse cosine decay. | |
| delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin( | |
| 0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1) | |
| ) | |
| else: | |
| delay_rate = 1.0 | |
| t = np.clip(step / max_steps, 0, 1) | |
| log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t) | |
| return delay_rate * log_lerp | |
| return helper | |
| def strip_lowerdiag(L): | |
| uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda") | |
| uncertainty[:, 0] = L[:, 0, 0] | |
| uncertainty[:, 1] = L[:, 0, 1] | |
| uncertainty[:, 2] = L[:, 0, 2] | |
| uncertainty[:, 3] = L[:, 1, 1] | |
| uncertainty[:, 4] = L[:, 1, 2] | |
| uncertainty[:, 5] = L[:, 2, 2] | |
| return uncertainty | |
| def strip_symmetric(sym): | |
| return strip_lowerdiag(sym) | |
| def gaussian_3d_coeff(xyzs, covs): | |
| # xyzs: [N, 3] | |
| # covs: [N, 6] | |
| x, y, z = xyzs[:, 0], xyzs[:, 1], xyzs[:, 2] | |
| a, b, c, d, e, f = covs[:, 0], covs[:, 1], covs[:, 2], covs[:, 3], covs[:, 4], covs[:, 5] | |
| # eps must be small enough !!! | |
| inv_det = 1 / (a * d * f + 2 * e * c * b - e**2 * a - c**2 * d - b**2 * f + 1e-24) | |
| inv_a = (d * f - e**2) * inv_det | |
| inv_b = (e * c - b * f) * inv_det | |
| inv_c = (e * b - c * d) * inv_det | |
| inv_d = (a * f - c**2) * inv_det | |
| inv_e = (b * c - e * a) * inv_det | |
| inv_f = (a * d - b**2) * inv_det | |
| power = -0.5 * (x**2 * inv_a + y**2 * inv_d + z**2 * inv_f) - x * y * inv_b - x * z * inv_c - y * z * inv_e | |
| power[power > 0] = -1e10 # abnormal values... make weights 0 | |
| return torch.exp(power) | |
| def build_rotation(r): | |
| norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3]) | |
| q = r / norm[:, None] | |
| R = torch.zeros((q.size(0), 3, 3), device='cuda') | |
| r = q[:, 0] | |
| x = q[:, 1] | |
| y = q[:, 2] | |
| z = q[:, 3] | |
| R[:, 0, 0] = 1 - 2 * (y*y + z*z) | |
| R[:, 0, 1] = 2 * (x*y - r*z) | |
| R[:, 0, 2] = 2 * (x*z + r*y) | |
| R[:, 1, 0] = 2 * (x*y + r*z) | |
| R[:, 1, 1] = 1 - 2 * (x*x + z*z) | |
| R[:, 1, 2] = 2 * (y*z - r*x) | |
| R[:, 2, 0] = 2 * (x*z - r*y) | |
| R[:, 2, 1] = 2 * (y*z + r*x) | |
| R[:, 2, 2] = 1 - 2 * (x*x + y*y) | |
| return R | |
| def build_scaling_rotation(s, r): | |
| L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda") | |
| R = build_rotation(r) | |
| L[:,0,0] = s[:,0] | |
| L[:,1,1] = s[:,1] | |
| L[:,2,2] = s[:,2] | |
| L = R @ L | |
| return L | |
| class BasicPointCloud(NamedTuple): | |
| points: np.array | |
| colors: np.array | |
| normals: np.array | |
| 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) | |
| def get_scaling(self): | |
| return self.scaling_activation(self._scaling) | |
| def get_rotation(self): | |
| return self.rotation_activation(self._rotation) | |
| def get_xyz(self): | |
| return self._xyz | |
| def get_features(self): | |
| features_dc = self._features_dc | |
| features_rest = self._features_rest | |
| return torch.cat((features_dc, features_rest), dim=1) | |
| def get_opacity(self): | |
| return self.opacity_activation(self._opacity) | |
| def extract_fields(self, resolution=128, num_blocks=16, relax_ratio=1.5): | |
| # resolution: resolution of field | |
| block_size = 2 / num_blocks | |
| assert resolution % block_size == 0 | |
| split_size = resolution // num_blocks | |
| opacities = self.get_opacity | |
| # pre-filter low opacity gaussians to save computation | |
| mask = (opacities > 0.005).squeeze(1) | |
| opacities = opacities[mask] | |
| xyzs = self.get_xyz[mask] | |
| stds = self.get_scaling[mask] | |
| # normalize to ~ [-1, 1] | |
| mn, mx = xyzs.amin(0), xyzs.amax(0) | |
| self.center = (mn + mx) / 2 | |
| self.scale = 1.8 / (mx - mn).amax().item() | |
| xyzs = (xyzs - self.center) * self.scale | |
| stds = stds * self.scale | |
| covs = self.covariance_activation(stds, 1, self._rotation[mask]) | |
| # tile | |
| device = opacities.device | |
| occ = torch.zeros([resolution] * 3, dtype=torch.float32, device=device) | |
| X = torch.linspace(-1, 1, resolution).split(split_size) | |
| Y = torch.linspace(-1, 1, resolution).split(split_size) | |
| Z = torch.linspace(-1, 1, resolution).split(split_size) | |
| # loop blocks (assume max size of gaussian is small than relax_ratio * block_size !!!) | |
| for xi, xs in enumerate(X): | |
| for yi, ys in enumerate(Y): | |
| for zi, zs in enumerate(Z): | |
| xx, yy, zz = torch.meshgrid(xs, ys, zs) | |
| # sample points [M, 3] | |
| pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1).to(device) | |
| # in-tile gaussians mask | |
| vmin, vmax = pts.amin(0), pts.amax(0) | |
| vmin -= block_size * relax_ratio | |
| vmax += block_size * relax_ratio | |
| mask = (xyzs < vmax).all(-1) & (xyzs > vmin).all(-1) | |
| # if hit no gaussian, continue to next block | |
| if not mask.any(): | |
| continue | |
| mask_xyzs = xyzs[mask] # [L, 3] | |
| mask_covs = covs[mask] # [L, 6] | |
| mask_opas = opacities[mask].view(1, -1) # [L, 1] --> [1, L] | |
| # query per point-gaussian pair. | |
| g_pts = pts.unsqueeze(1).repeat(1, mask_covs.shape[0], 1) - mask_xyzs.unsqueeze(0) # [M, L, 3] | |
| g_covs = mask_covs.unsqueeze(0).repeat(pts.shape[0], 1, 1) # [M, L, 6] | |
| # batch on gaussian to avoid OOM | |
| batch_g = 1024 | |
| val = 0 | |
| for start in range(0, g_covs.shape[1], batch_g): | |
| end = min(start + batch_g, g_covs.shape[1]) | |
| w = gaussian_3d_coeff(g_pts[:, start:end].reshape(-1, 3), g_covs[:, start:end].reshape(-1, 6)).reshape(pts.shape[0], -1) # [M, l] | |
| val += (mask_opas[:, start:end] * w).sum(-1) | |
| # kiui.lo(val, mask_opas, w) | |
| occ[xi * split_size: xi * split_size + len(xs), | |
| yi * split_size: yi * split_size + len(ys), | |
| zi * split_size: zi * split_size + len(zs)] = val.reshape(len(xs), len(ys), len(zs)) | |
| kiui.lo(occ, verbose=1) | |
| return occ | |
| def extract_mesh(self, path, density_thresh=1, resolution=128, decimate_target=1e5): | |
| os.makedirs(os.path.dirname(path), exist_ok=True) | |
| occ = self.extract_fields(resolution).detach().cpu().numpy() | |
| import mcubes | |
| vertices, triangles = mcubes.marching_cubes(occ, density_thresh) | |
| vertices = vertices / (resolution - 1.0) * 2 - 1 | |
| # transform back to the original space | |
| vertices = vertices / self.scale + self.center.detach().cpu().numpy() | |
| vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.015) | |
| if decimate_target > 0 and triangles.shape[0] > decimate_target: | |
| vertices, triangles = decimate_mesh(vertices, triangles, decimate_target) | |
| v = torch.from_numpy(vertices.astype(np.float32)).contiguous().cuda() | |
| f = torch.from_numpy(triangles.astype(np.int32)).contiguous().cuda() | |
| print( | |
| f"[INFO] marching cubes result: {v.shape} ({v.min().item()}-{v.max().item()}), {f.shape}" | |
| ) | |
| mesh = Mesh(v=v, f=f, device='cuda') | |
| return mesh | |
| 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 = 1): | |
| 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.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): | |
| 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 save_ply(self, path): | |
| os.makedirs(os.path.dirname(path), exist_ok=True) | |
| 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] | |
| print("Number of points at loading : ", xyz.shape[0]) | |
| 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_")] | |
| 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_")] | |
| 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")] | |
| 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: | |
| stored_state = self.optimizer.state.get(group['params'][0], None) | |
| if stored_state is not None: | |
| stored_state["exp_avg"] = stored_state["exp_avg"][mask] | |
| stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask] | |
| del self.optimizer.state[group['params'][0]] | |
| group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True))) | |
| self.optimizer.state[group['params'][0]] = stored_state | |
| optimizable_tensors[group["name"]] = group["params"][0] | |
| else: | |
| group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True)) | |
| optimizable_tensors[group["name"]] = group["params"][0] | |
| return optimizable_tensors | |
| def prune_points(self, mask): | |
| valid_points_mask = ~mask | |
| optimizable_tensors = self._prune_optimizer(valid_points_mask) | |
| self._xyz = optimizable_tensors["xyz"] | |
| self._features_dc = optimizable_tensors["f_dc"] | |
| self._features_rest = optimizable_tensors["f_rest"] | |
| self._opacity = optimizable_tensors["opacity"] | |
| self._scaling = optimizable_tensors["scaling"] | |
| self._rotation = optimizable_tensors["rotation"] | |
| self.xyz_gradient_accum = self.xyz_gradient_accum[valid_points_mask] | |
| self.denom = self.denom[valid_points_mask] | |
| self.max_radii2D = self.max_radii2D[valid_points_mask] | |
| def cat_tensors_to_optimizer(self, tensors_dict): | |
| optimizable_tensors = {} | |
| for group in self.optimizer.param_groups: | |
| assert len(group["params"]) == 1 | |
| extension_tensor = tensors_dict[group["name"]] | |
| stored_state = self.optimizer.state.get(group['params'][0], None) | |
| if stored_state is not None: | |
| stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0) | |
| stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0) | |
| del self.optimizer.state[group['params'][0]] | |
| group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) | |
| self.optimizer.state[group['params'][0]] = stored_state | |
| optimizable_tensors[group["name"]] = group["params"][0] | |
| else: | |
| group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True)) | |
| optimizable_tensors[group["name"]] = group["params"][0] | |
| return optimizable_tensors | |
| def densification_postfix(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation): | |
| 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) | |
| 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) | |
| prune_filter = torch.cat((selected_pts_mask, torch.zeros(N * selected_pts_mask.sum(), device="cuda", dtype=bool))) | |
| self.prune_points(prune_filter) | |
| def densify_and_clone(self, grads, grad_threshold, scene_extent): | |
| # Extract points that satisfy the gradient condition | |
| selected_pts_mask = torch.where(torch.norm(grads, dim=-1) >= grad_threshold, True, False) | |
| selected_pts_mask = torch.logical_and(selected_pts_mask, | |
| torch.max(self.get_scaling, dim=1).values <= self.percent_dense*scene_extent) | |
| new_xyz = self._xyz[selected_pts_mask] | |
| new_features_dc = self._features_dc[selected_pts_mask] | |
| new_features_rest = self._features_rest[selected_pts_mask] | |
| new_opacities = self._opacity[selected_pts_mask] | |
| new_scaling = self._scaling[selected_pts_mask] | |
| new_rotation = self._rotation[selected_pts_mask] | |
| 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): | |
| grads = self.xyz_gradient_accum / self.denom | |
| grads[grads.isnan()] = 0.0 | |
| self.densify_and_clone(grads, max_grad, extent) | |
| self.densify_and_split(grads, max_grad, extent) | |
| prune_mask = (self.get_opacity < min_opacity).squeeze() | |
| if max_screen_size: | |
| big_points_vs = self.max_radii2D > max_screen_size | |
| big_points_ws = self.get_scaling.max(dim=1).values > 0.1 * extent | |
| prune_mask = torch.logical_or(torch.logical_or(prune_mask, big_points_vs), big_points_ws) | |
| self.prune_points(prune_mask) | |
| torch.cuda.empty_cache() | |
| def prune(self, 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(torch.logical_or(prune_mask, big_points_vs), big_points_ws) | |
| self.prune_points(prune_mask) | |
| torch.cuda.empty_cache() | |
| def add_densification_stats(self, viewspace_point_tensor, update_filter): | |
| self.xyz_gradient_accum[update_filter] += torch.norm(viewspace_point_tensor.grad[update_filter,:2], dim=-1, keepdim=True) | |
| self.denom[update_filter] += 1 | |
| def getProjectionMatrix(znear, zfar, fovX, fovY): | |
| tanHalfFovY = math.tan((fovY / 2)) | |
| tanHalfFovX = math.tan((fovX / 2)) | |
| P = torch.zeros(4, 4) | |
| z_sign = 1.0 | |
| P[0, 0] = 1 / tanHalfFovX | |
| P[1, 1] = 1 / tanHalfFovY | |
| P[3, 2] = z_sign | |
| P[2, 2] = z_sign * zfar / (zfar - znear) | |
| P[2, 3] = -(zfar * znear) / (zfar - znear) | |
| return P | |
| class MiniCam: | |
| def __init__(self, c2w, width, height, fovy, fovx, znear, zfar): | |
| # c2w (pose) should be in NeRF convention. | |
| self.image_width = width | |
| self.image_height = height | |
| self.FoVy = fovy | |
| self.FoVx = fovx | |
| self.znear = znear | |
| self.zfar = zfar | |
| w2c = np.linalg.inv(c2w) | |
| # rectify... | |
| w2c[1:3, :3] *= -1 | |
| w2c[:3, 3] *= -1 | |
| self.world_view_transform = torch.tensor(w2c).transpose(0, 1).cuda() | |
| self.projection_matrix = ( | |
| getProjectionMatrix( | |
| znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy | |
| ) | |
| .transpose(0, 1) | |
| .cuda() | |
| ) | |
| self.full_proj_transform = self.world_view_transform @ self.projection_matrix | |
| self.camera_center = -torch.tensor(c2w[:3, 3]).cuda() | |
| class Renderer: | |
| def __init__(self, sh_degree=3, white_background=True, radius=1): | |
| self.sh_degree = sh_degree | |
| self.white_background = white_background | |
| self.radius = radius | |
| self.gaussians = GaussianModel(sh_degree) | |
| self.bg_color = torch.tensor( | |
| [1, 1, 1] if white_background else [0, 0, 0], | |
| dtype=torch.float32, | |
| device="cuda", | |
| ) | |
| def initialize(self, input=None, num_pts=5000, radius=0.5): | |
| # load checkpoint | |
| if input is None: | |
| # init from random point cloud | |
| phis = np.random.random((num_pts,)) * 2 * np.pi | |
| costheta = np.random.random((num_pts,)) * 2 - 1 | |
| thetas = np.arccos(costheta) | |
| mu = np.random.random((num_pts,)) | |
| radius = radius * np.cbrt(mu) | |
| x = radius * np.sin(thetas) * np.cos(phis) | |
| y = radius * np.sin(thetas) * np.sin(phis) | |
| z = radius * np.cos(thetas) | |
| xyz = np.stack((x, y, z), axis=1) | |
| # xyz = np.random.random((num_pts, 3)) * 2.6 - 1.3 | |
| shs = np.random.random((num_pts, 3)) / 255.0 | |
| pcd = BasicPointCloud( | |
| points=xyz, colors=SH2RGB(shs), normals=np.zeros((num_pts, 3)) | |
| ) | |
| self.gaussians.create_from_pcd(pcd, 10) | |
| elif isinstance(input, BasicPointCloud): | |
| # load from a provided pcd | |
| self.gaussians.create_from_pcd(input, 1) | |
| else: | |
| # load from saved ply | |
| self.gaussians.load_ply(input) | |
| def render( | |
| self, | |
| viewpoint_camera, | |
| scaling_modifier=1.0, | |
| invert_bg_color=False, | |
| override_color=None, | |
| compute_cov3D_python=False, | |
| convert_SHs_python=False, | |
| ): | |
| # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means | |
| screenspace_points = ( | |
| torch.zeros_like( | |
| self.gaussians.get_xyz, | |
| dtype=self.gaussians.get_xyz.dtype, | |
| requires_grad=True, | |
| device="cuda", | |
| ) | |
| + 0 | |
| ) | |
| try: | |
| screenspace_points.retain_grad() | |
| except: | |
| pass | |
| # Set up rasterization configuration | |
| tanfovx = math.tan(viewpoint_camera.FoVx * 0.5) | |
| tanfovy = math.tan(viewpoint_camera.FoVy * 0.5) | |
| raster_settings = GaussianRasterizationSettings( | |
| image_height=int(viewpoint_camera.image_height), | |
| image_width=int(viewpoint_camera.image_width), | |
| tanfovx=tanfovx, | |
| tanfovy=tanfovy, | |
| bg=self.bg_color if not invert_bg_color else 1 - self.bg_color, | |
| scale_modifier=scaling_modifier, | |
| viewmatrix=viewpoint_camera.world_view_transform, | |
| projmatrix=viewpoint_camera.full_proj_transform, | |
| sh_degree=self.gaussians.active_sh_degree, | |
| campos=viewpoint_camera.camera_center, | |
| prefiltered=False, | |
| debug=False, | |
| ) | |
| rasterizer = GaussianRasterizer(raster_settings=raster_settings) | |
| means3D = self.gaussians.get_xyz | |
| means2D = screenspace_points | |
| opacity = self.gaussians.get_opacity | |
| # If precomputed 3d covariance is provided, use it. If not, then it will be computed from | |
| # scaling / rotation by the rasterizer. | |
| scales = None | |
| rotations = None | |
| cov3D_precomp = None | |
| if compute_cov3D_python: | |
| cov3D_precomp = self.gaussians.get_covariance(scaling_modifier) | |
| else: | |
| scales = self.gaussians.get_scaling | |
| rotations = self.gaussians.get_rotation | |
| # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors | |
| # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer. | |
| shs = None | |
| colors_precomp = None | |
| if colors_precomp is None: | |
| if convert_SHs_python: | |
| shs_view = self.gaussians.get_features.transpose(1, 2).view( | |
| -1, 3, (self.gaussians.max_sh_degree + 1) ** 2 | |
| ) | |
| dir_pp = self.gaussians.get_xyz - viewpoint_camera.camera_center.repeat( | |
| self.gaussians.get_features.shape[0], 1 | |
| ) | |
| dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True) | |
| sh2rgb = eval_sh( | |
| self.gaussians.active_sh_degree, shs_view, dir_pp_normalized | |
| ) | |
| colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) | |
| else: | |
| shs = self.gaussians.get_features | |
| else: | |
| colors_precomp = override_color | |
| # Rasterize visible Gaussians to image, obtain their radii (on screen). | |
| rendered_image, radii, rendered_depth, rendered_alpha = rasterizer( | |
| means3D=means3D, | |
| means2D=means2D, | |
| shs=shs, | |
| colors_precomp=colors_precomp, | |
| opacities=opacity, | |
| scales=scales, | |
| rotations=rotations, | |
| cov3D_precomp=cov3D_precomp, | |
| ) | |
| rendered_image = rendered_image.clamp(0, 1) | |
| # Those Gaussians that were frustum culled or had a radius of 0 were not visible. | |
| # They will be excluded from value updates used in the splitting criteria. | |
| return { | |
| "image": rendered_image, | |
| "depth": rendered_depth, | |
| "alpha": rendered_alpha, | |
| "viewspace_points": screenspace_points, | |
| "visibility_filter": radii > 0, | |
| "radii": radii, | |
| } | |