import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import logging import mcubes from icecream import ic import os import trimesh from pysdf import SDF from uni_rep.rep_3d.dmtet import marching_tets_tetmesh, create_tetmesh_variables def create_mt_variable(device): triangle_table = torch.tensor( [ [-1, -1, -1, -1, -1, -1], [1, 0, 2, -1, -1, -1], [4, 0, 3, -1, -1, -1], [1, 4, 2, 1, 3, 4], [3, 1, 5, -1, -1, -1], [2, 3, 0, 2, 5, 3], [1, 4, 0, 1, 5, 4], [4, 2, 5, -1, -1, -1], [4, 5, 2, -1, -1, -1], [4, 1, 0, 4, 5, 1], [3, 2, 0, 3, 5, 2], [1, 3, 5, -1, -1, -1], [4, 1, 2, 4, 3, 1], [3, 0, 4, -1, -1, -1], [2, 0, 1, -1, -1, -1], [-1, -1, -1, -1, -1, -1] ], dtype=torch.long, device=device) num_triangles_table = torch.tensor([0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long, device=device) base_tet_edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=device) v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=device)) return triangle_table, num_triangles_table, base_tet_edges, v_id def extract_fields_from_tets(bound_min, bound_max, resolution, query_func): # load tet via resolution # # scale them via bounds # # extract the geometry # # /home/xueyi/gen/DeepMetaHandles/data/tets/100_compress.npz # strange # device = bound_min.device # if resolution in [64, 70, 80, 90, 100]: # tet_fn = f"/home/xueyi/gen/DeepMetaHandles/data/tets/{resolution}_compress.npz" # else: tet_fn = f"/home/xueyi/gen/DeepMetaHandles/data/tets/{100}_compress.npz" tets = np.load(tet_fn) verts = torch.from_numpy(tets['vertices']).float().to(device) # verts positions indices = torch.from_numpy(tets['tets']).long().to(device) # .to(self.device) # split # # verts; verts; # minn_verts, _ = torch.min(verts, dim=0) maxx_verts, _ = torch.max(verts, dim=0) # (3, ) # exporting the # scale_verts = maxx_verts - minn_verts scale_bounds = bound_max - bound_min # scale bounds # ### scale the vertices ### scaled_verts = (verts - minn_verts.unsqueeze(0)) / (maxx_verts - minn_verts).unsqueeze(0) ### the maxx and minn verts scales ### # scaled_verts = (verts - minn_verts.unsqueeze(0)) / (maxx_verts - minn_verts).unsqueeze(0) ### the maxx and minn verts scales ### scaled_verts = scaled_verts * 2. - 1. # init the sdf filed viathe tet mesh vertices and the sdf values ## # scaled_verts = (scaled_verts * scale_bounds.unsqueeze(0)) + bound_min.unsqueeze(0) ## the scaled verts ### # scaled_verts = scaled_verts - scale_bounds.unsqueeze(0) / 2. # # scaled_verts = scaled_verts - bound_min.unsqueeze(0) - scale_bounds.unsqueeze(0) / 2. sdf_values = [] N = 64 query_bundles = N ** 3 ### N^3 query_NNs = scaled_verts.size(0) // query_bundles if query_NNs * query_bundles < scaled_verts.size(0): query_NNs += 1 for i_query in range(query_NNs): cur_bundle_st = i_query * query_bundles cur_bundle_ed = (i_query + 1) * query_bundles cur_bundle_ed = min(cur_bundle_ed, scaled_verts.size(0)) cur_query_pts = scaled_verts[cur_bundle_st: cur_bundle_ed] cur_query_vals = query_func(cur_query_pts) sdf_values.append(cur_query_vals) sdf_values = torch.cat(sdf_values, dim=0) # print(f"queryed sdf values: {sdf_values.size()}") # GT_sdf_values = np.load("/home/xueyi/diffsim/DiffHand/assets/hand/100_sdf_values.npy", allow_pickle=True) GT_sdf_values = torch.from_numpy(GT_sdf_values).float().to(device) # intrinsic, tet values, pts values, sdf network # triangle_table, num_triangles_table, base_tet_edges, v_id = create_mt_variable(device) tet_table, num_tets_table = create_tetmesh_variables(device) sdf_values = sdf_values.squeeze(-1) # how the rendering # # print(f"GT_sdf_values: {GT_sdf_values.size()}, sdf_values: {sdf_values.size()}, scaled_verts: {scaled_verts.size()}") # print(f"scaled_verts: {scaled_verts.size()}, ") # pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id, # return_tet_mesh=False, ori_v=None, num_tets_table=None, tet_table=None): # marching_tets_tetmesh ## verts, faces, tet_verts, tets = marching_tets_tetmesh(scaled_verts, sdf_values, indices, triangle_table, num_triangles_table, base_tet_edges, v_id, return_tet_mesh=True, ori_v=scaled_verts, num_tets_table=num_tets_table, tet_table=tet_table) ### use the GT sdf values for the marching tets ### GT_verts, GT_faces, GT_tet_verts, GT_tets = marching_tets_tetmesh(scaled_verts, GT_sdf_values, indices, triangle_table, num_triangles_table, base_tet_edges, v_id, return_tet_mesh=True, ori_v=scaled_verts, num_tets_table=num_tets_table, tet_table=tet_table) # print(f"After tet marching with verts: {verts.size()}, faces: {faces.size()}") return verts, faces, sdf_values, GT_verts, GT_faces # verts, faces # def extract_fields(bound_min, bound_max, resolution, query_func): N = 64 X = torch.linspace(bound_min[0], bound_max[0], resolution).split(N) Y = torch.linspace(bound_min[1], bound_max[1], resolution).split(N) Z = torch.linspace(bound_min[2], bound_max[2], resolution).split(N) u = np.zeros([resolution, resolution, resolution], dtype=np.float32) with torch.no_grad(): 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) pts = torch.cat([xx.reshape(-1, 1), yy.reshape(-1, 1), zz.reshape(-1, 1)], dim=-1) val = query_func(pts).reshape(len(xs), len(ys), len(zs)).detach().cpu().numpy() u[xi * N: xi * N + len(xs), yi * N: yi * N + len(ys), zi * N: zi * N + len(zs)] = val # should save u here # # save_u_path = os.path.join("/data2/datasets/diffsim/neus/exp/hand_test/womask_sphere_reverse_value/other_saved", "sdf_values.npy") # np.save(save_u_path, u) # print(f"u saved to {save_u_path}") return u def extract_geometry(bound_min, bound_max, resolution, threshold, query_func): print('threshold: {}'.format(threshold)) ## using maching cubes ### u = extract_fields(bound_min, bound_max, resolution, query_func) vertices, triangles = mcubes.marching_cubes(u, threshold) # grid sdf and marching cubes # b_max_np = bound_max.detach().cpu().numpy() b_min_np = bound_min.detach().cpu().numpy() vertices = vertices / (resolution - 1.0) * (b_max_np - b_min_np)[None, :] + b_min_np[None, :] ### using maching cubes ### ### using marching tets ### # vertices, triangles = extract_fields_from_tets(bound_min, bound_max, resolution, query_func) # vertices = vertices.detach().cpu().numpy() # triangles = triangles.detach().cpu().numpy() ### using marching tets ### # b_max_np = bound_max.detach().cpu().numpy() # b_min_np = bound_min.detach().cpu().numpy() # vertices = vertices / (resolution - 1.0) * (b_max_np - b_min_np)[None, :] + b_min_np[None, :] return vertices, triangles def extract_geometry_tets(bound_min, bound_max, resolution, threshold, query_func): # print('threshold: {}'.format(threshold)) ### using maching cubes ### # u = extract_fields(bound_min, bound_max, resolution, query_func) # vertices, triangles = mcubes.marching_cubes(u, threshold) # grid sdf and marching cubes # # b_max_np = bound_max.detach().cpu().numpy() # b_min_np = bound_min.detach().cpu().numpy() # vertices = vertices / (resolution - 1.0) * (b_max_np - b_min_np)[None, :] + b_min_np[None, :] ### using maching cubes ### ## ### using marching tets ### fiels from tets ## vertices, triangles, tet_sdf_values, GT_verts, GT_faces = extract_fields_from_tets(bound_min, bound_max, resolution, query_func) # vertices = vertices.detach().cpu().numpy() # triangles = triangles.detach().cpu().numpy() ### using marching tets ### # b_max_np = bound_max.detach().cpu().numpy() # b_min_np = bound_min.detach().cpu().numpy() # # vertices = vertices / (resolution - 1.0) * (b_max_np - b_min_np)[None, :] + b_min_np[None, :] return vertices, triangles, tet_sdf_values, GT_verts, GT_faces def sample_pdf(bins, weights, n_samples, det=False): # This implementation is from NeRF # Get pdf weights = weights + 1e-5 # prevent nans pdf = weights / torch.sum(weights, -1, keepdim=True) cdf = torch.cumsum(pdf, -1) cdf = torch.cat([torch.zeros_like(cdf[..., :1]), cdf], -1) # Take uniform samples if det: u = torch.linspace(0. + 0.5 / n_samples, 1. - 0.5 / n_samples, steps=n_samples) u = u.expand(list(cdf.shape[:-1]) + [n_samples]) else: u = torch.rand(list(cdf.shape[:-1]) + [n_samples]) # Invert CDF # invert cdf # u = u.contiguous() inds = torch.searchsorted(cdf, u, right=True) below = torch.max(torch.zeros_like(inds - 1), inds - 1) above = torch.min((cdf.shape[-1] - 1) * torch.ones_like(inds), inds) inds_g = torch.stack([below, above], -1) # (batch, N_samples, 2) matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]] cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g) bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g) denom = (cdf_g[..., 1] - cdf_g[..., 0]) denom = torch.where(denom < 1e-5, torch.ones_like(denom), denom) t = (u - cdf_g[..., 0]) / denom samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0]) return samples def load_GT_vertices(GT_meshes_folder): tot_meshes_fns = os.listdir(GT_meshes_folder) tot_meshes_fns = [fn for fn in tot_meshes_fns if fn.endswith(".obj")] tot_mesh_verts = [] tot_mesh_faces = [] n_tot_verts = 0 for fn in tot_meshes_fns: cur_mesh_fn = os.path.join(GT_meshes_folder, fn) obj_mesh = trimesh.load(cur_mesh_fn, process=False) # obj_mesh.remove_degenerate_faces(height=1e-06) verts_obj = np.array(obj_mesh.vertices) faces_obj = np.array(obj_mesh.faces) tot_mesh_verts.append(verts_obj) tot_mesh_faces.append(faces_obj + n_tot_verts) n_tot_verts += verts_obj.shape[0] # tot_mesh_faces.append(faces_obj) tot_mesh_verts = np.concatenate(tot_mesh_verts, axis=0) tot_mesh_faces = np.concatenate(tot_mesh_faces, axis=0) return tot_mesh_verts, tot_mesh_faces class NeuSRenderer: def __init__(self, nerf, sdf_network, deviation_network, color_network, n_samples, n_importance, n_outside, up_sample_steps, perturb): self.nerf = nerf self.sdf_network = sdf_network self.deviation_network = deviation_network self.color_network = color_network self.n_samples = n_samples self.n_importance = n_importance self.n_outside = n_outside self.up_sample_steps = up_sample_steps self.perturb = perturb GT_meshes_folder = "/home/xueyi/diffsim/DiffHand/assets/hand" self.mesh_vertices, self.mesh_faces = load_GT_vertices(GT_meshes_folder=GT_meshes_folder) maxx_pts = 25. minn_pts = -15. self.mesh_vertices = (self.mesh_vertices - minn_pts) / (maxx_pts - minn_pts) f = SDF(self.mesh_vertices, self.mesh_faces) self.gt_sdf = f ## a unite sphere or box self.minn_pts = 0 self.maxx_pts = 1. # self.minn_pts = -1.5 # self.maxx_pts = 1.5 self.bkg_pts = ... # TODO: the bkg pts self.cur_fr_bkg_pts_defs = ... # TODO: set the cur_bkg_pts_defs for each frame # self.dist_interp_thres = ... # TODO # get the pts and render the pts # # pts and the rendering pts # def deform_pts(self, pts): # pts: nn_batch x nn_samples x 3 nnb, nns = pts.size(0), pts.size(1) pts_exp = pts.contiguous().view(nnb * nns, -1).contiguous() dist_pts_to_bkg_pts = torch.sum( (pts_exp.unsqueeze(1) - self.bkg_pts.unsqueeze(0)) ** 2, dim=-1 ## nn_pts_exp x nn_bkg_pts ) def render_core_outside(self, rays_o, rays_d, z_vals, sample_dist, nerf, background_rgb=None): """ Render background """ batch_size, n_samples = z_vals.shape # Section length dists = z_vals[..., 1:] - z_vals[..., :-1] dists = torch.cat([dists, torch.Tensor([sample_dist]).expand(dists[..., :1].shape)], -1) mid_z_vals = z_vals + dists * 0.5 # Section midpoints # pts = rays_o[:, None, :] + rays_d[:, None, :] * mid_z_vals[..., :, None] # batch_size, n_samples, 3 # # pts = pts.flip((-1,)) * 2 - 1 pts = pts * 2 - 1 dis_to_center = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=True).clip(1.0, 1e10) pts = torch.cat([pts / dis_to_center, 1.0 / dis_to_center], dim=-1) # batch_size, n_samples, 4 # dirs = rays_d[:, None, :].expand(batch_size, n_samples, 3) pts = pts.reshape(-1, 3 + int(self.n_outside > 0)) dirs = dirs.reshape(-1, 3) density, sampled_color = nerf(pts, dirs) sampled_color = torch.sigmoid(sampled_color) alpha = 1.0 - torch.exp(-F.softplus(density.reshape(batch_size, n_samples)) * dists) alpha = alpha.reshape(batch_size, n_samples) weights = alpha * torch.cumprod(torch.cat([torch.ones([batch_size, 1]), 1. - alpha + 1e-7], -1), -1)[:, :-1] sampled_color = sampled_color.reshape(batch_size, n_samples, 3) color = (weights[:, :, None] * sampled_color).sum(dim=1) if background_rgb is not None: color = color + background_rgb * (1.0 - weights.sum(dim=-1, keepdim=True)) return { 'color': color, 'sampled_color': sampled_color, 'alpha': alpha, 'weights': weights, } def up_sample(self, rays_o, rays_d, z_vals, sdf, n_importance, inv_s): """ Up sampling give a fixed inv_s """ batch_size, n_samples = z_vals.shape pts = rays_o[:, None, :] + rays_d[:, None, :] * z_vals[..., :, None] # n_rays, n_samples, 3 # pts = pts.flip((-1,)) * 2 - 1 pts = pts * 2 - 1 radius = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=False) inside_sphere = (radius[:, :-1] < 1.0) | (radius[:, 1:] < 1.0) sdf = sdf.reshape(batch_size, n_samples) prev_sdf, next_sdf = sdf[:, :-1], sdf[:, 1:] prev_z_vals, next_z_vals = z_vals[:, :-1], z_vals[:, 1:] mid_sdf = (prev_sdf + next_sdf) * 0.5 cos_val = (next_sdf - prev_sdf) / (next_z_vals - prev_z_vals + 1e-5) # ---------------------------------------------------------------------------------------------------------- # Use min value of [ cos, prev_cos ] # Though it makes the sampling (not rendering) a little bit biased, this strategy can make the sampling more # robust when meeting situations like below: # # SDF # ^ # |\ -----x----... # | \ / # | x x # |---\----/-------------> 0 level # | \ / # | \/ # | # ---------------------------------------------------------------------------------------------------------- prev_cos_val = torch.cat([torch.zeros([batch_size, 1]), cos_val[:, :-1]], dim=-1) cos_val = torch.stack([prev_cos_val, cos_val], dim=-1) cos_val, _ = torch.min(cos_val, dim=-1, keepdim=False) cos_val = cos_val.clip(-1e3, 0.0) * inside_sphere dist = (next_z_vals - prev_z_vals) prev_esti_sdf = mid_sdf - cos_val * dist * 0.5 next_esti_sdf = mid_sdf + cos_val * dist * 0.5 prev_cdf = torch.sigmoid(prev_esti_sdf * inv_s) next_cdf = torch.sigmoid(next_esti_sdf * inv_s) alpha = (prev_cdf - next_cdf + 1e-5) / (prev_cdf + 1e-5) weights = alpha * torch.cumprod( torch.cat([torch.ones([batch_size, 1]), 1. - alpha + 1e-7], -1), -1)[:, :-1] z_samples = sample_pdf(z_vals, weights, n_importance, det=True).detach() return z_samples def cat_z_vals(self, rays_o, rays_d, z_vals, new_z_vals, sdf, last=False): batch_size, n_samples = z_vals.shape _, n_importance = new_z_vals.shape pts = rays_o[:, None, :] + rays_d[:, None, :] * new_z_vals[..., :, None] # pts = pts.flip((-1,)) * 2 - 1 pts = pts * 2 - 1 z_vals = torch.cat([z_vals, new_z_vals], dim=-1) z_vals, index = torch.sort(z_vals, dim=-1) if not last: new_sdf = self.sdf_network.sdf(pts.reshape(-1, 3)).reshape(batch_size, n_importance) sdf = torch.cat([sdf, new_sdf], dim=-1) xx = torch.arange(batch_size)[:, None].expand(batch_size, n_samples + n_importance).reshape(-1) index = index.reshape(-1) sdf = sdf[(xx, index)].reshape(batch_size, n_samples + n_importance) return z_vals, sdf def render_core(self, rays_o, rays_d, z_vals, sample_dist, sdf_network, deviation_network, color_network, background_alpha=None, background_sampled_color=None, background_rgb=None, cos_anneal_ratio=0.0): batch_size, n_samples = z_vals.shape # Section length dists = z_vals[..., 1:] - z_vals[..., :-1] dists = torch.cat([dists, torch.Tensor([sample_dist]).expand(dists[..., :1].shape)], -1) mid_z_vals = z_vals + dists * 0.5 # z_vals and dists * 0.5 # # Section midpoints pts = rays_o[:, None, :] + rays_d[:, None, :] * mid_z_vals[..., :, None] # n_rays, n_samples, 3 dirs = rays_d[:, None, :].expand(pts.shape) pts = pts.reshape(-1, 3) # pts, nn_ou dirs = dirs.reshape(-1, 3) pts = (pts - self.minn_pts) / (self.maxx_pts - self.minn_pts) # pts = pts.flip((-1,)) * 2 - 1 # pts = pts * 2 - 1 sdf_nn_output = sdf_network(pts) sdf = sdf_nn_output[:, :1] feature_vector = sdf_nn_output[:, 1:] gradients = sdf_network.gradient(pts).squeeze() sampled_color = color_network(pts, gradients, dirs, feature_vector).reshape(batch_size, n_samples, 3) # deviation network # inv_s = deviation_network(torch.zeros([1, 3]))[:, :1].clip(1e-6, 1e6) # Single parameter inv_s = inv_s.expand(batch_size * n_samples, 1) true_cos = (dirs * gradients).sum(-1, keepdim=True) # "cos_anneal_ratio" grows from 0 to 1 in the beginning training iterations. The anneal strategy below makes # the cos value "not dead" at the beginning training iterations, for better convergence. iter_cos = -(F.relu(-true_cos * 0.5 + 0.5) * (1.0 - cos_anneal_ratio) + F.relu(-true_cos) * cos_anneal_ratio) # always non-positive # Estimate signed distances at section points estimated_next_sdf = sdf + iter_cos * dists.reshape(-1, 1) * 0.5 estimated_prev_sdf = sdf - iter_cos * dists.reshape(-1, 1) * 0.5 prev_cdf = torch.sigmoid(estimated_prev_sdf * inv_s) next_cdf = torch.sigmoid(estimated_next_sdf * inv_s) p = prev_cdf - next_cdf c = prev_cdf alpha = ((p + 1e-5) / (c + 1e-5)).reshape(batch_size, n_samples).clip(0.0, 1.0) pts_norm = torch.linalg.norm(pts, ord=2, dim=-1, keepdim=True).reshape(batch_size, n_samples) inside_sphere = (pts_norm < 1.0).float().detach() relax_inside_sphere = (pts_norm < 1.2).float().detach() # Render with background if background_alpha is not None: alpha = alpha * inside_sphere + background_alpha[:, :n_samples] * (1.0 - inside_sphere) alpha = torch.cat([alpha, background_alpha[:, n_samples:]], dim=-1) sampled_color = sampled_color * inside_sphere[:, :, None] +\ background_sampled_color[:, :n_samples] * (1.0 - inside_sphere)[:, :, None] sampled_color = torch.cat([sampled_color, background_sampled_color[:, n_samples:]], dim=1) weights = alpha * torch.cumprod(torch.cat([torch.ones([batch_size, 1]), 1. - alpha + 1e-7], -1), -1)[:, :-1] weights_sum = weights.sum(dim=-1, keepdim=True) color = (sampled_color * weights[:, :, None]).sum(dim=1) if background_rgb is not None: # Fixed background, usually black color = color + background_rgb * (1.0 - weights_sum) # Eikonal loss gradient_error = (torch.linalg.norm(gradients.reshape(batch_size, n_samples, 3), ord=2, dim=-1) - 1.0) ** 2 gradient_error = (relax_inside_sphere * gradient_error).sum() / (relax_inside_sphere.sum() + 1e-5) return { 'color': color, 'sdf': sdf, 'dists': dists, 'gradients': gradients.reshape(batch_size, n_samples, 3), 's_val': 1.0 / inv_s, 'mid_z_vals': mid_z_vals, 'weights': weights, 'cdf': c.reshape(batch_size, n_samples), 'gradient_error': gradient_error, 'inside_sphere': inside_sphere } def render(self, rays_o, rays_d, near, far, perturb_overwrite=-1, background_rgb=None, cos_anneal_ratio=0.0, use_gt_sdf=False): batch_size = len(rays_o) sample_dist = 2.0 / self.n_samples # Assuming the region of interest is a unit sphere z_vals = torch.linspace(0.0, 1.0, self.n_samples) z_vals = near + (far - near) * z_vals[None, :] z_vals_outside = None if self.n_outside > 0: z_vals_outside = torch.linspace(1e-3, 1.0 - 1.0 / (self.n_outside + 1.0), self.n_outside) n_samples = self.n_samples perturb = self.perturb if perturb_overwrite >= 0: perturb = perturb_overwrite if perturb > 0: t_rand = (torch.rand([batch_size, 1]) - 0.5) z_vals = z_vals + t_rand * 2.0 / self.n_samples if self.n_outside > 0: mids = .5 * (z_vals_outside[..., 1:] + z_vals_outside[..., :-1]) upper = torch.cat([mids, z_vals_outside[..., -1:]], -1) lower = torch.cat([z_vals_outside[..., :1], mids], -1) t_rand = torch.rand([batch_size, z_vals_outside.shape[-1]]) z_vals_outside = lower[None, :] + (upper - lower)[None, :] * t_rand if self.n_outside > 0: z_vals_outside = far / torch.flip(z_vals_outside, dims=[-1]) + 1.0 / self.n_samples background_alpha = None background_sampled_color = None # Up sample if self.n_importance > 0: with torch.no_grad(): pts = rays_o[:, None, :] + rays_d[:, None, :] * z_vals[..., :, None] pts = (pts - self.minn_pts) / (self.maxx_pts - self.minn_pts) # sdf = self.sdf_network.sdf(pts.reshape(-1, 3)).reshape(batch_size, self.n_samples) # gt_sdf # # # pts = ((pts - xyz_min) / (xyz_max - xyz_min)).flip((-1,)) * 2 - 1 # pts = pts.flip((-1,)) * 2 - 1 pts = pts * 2 - 1 pts_exp = pts.reshape(-1, 3) # minn_pts, _ = torch.min(pts_exp, dim=0) # maxx_pts, _ = torch.max(pts_exp, dim=0) # print(f"minn_pts: {minn_pts}, maxx_pts: {maxx_pts}") # pts_to_near = pts - near.unsqueeze(1) # maxx_pts = 1.5; minn_pts = -1.5 # # maxx_pts = 3; minn_pts = -3 # # maxx_pts = 1; minn_pts = -1 # pts_exp = (pts_exp - minn_pts) / (maxx_pts - minn_pts) ## render and iamges #### if use_gt_sdf: ### use the GT sdf field #### # print(f"Using gt sdf :") sdf = self.gt_sdf(pts_exp.reshape(-1, 3).detach().cpu().numpy()) sdf = torch.from_numpy(sdf).float().cuda() sdf = sdf.reshape(batch_size, self.n_samples) ### use the GT sdf field #### else: #### use the optimized sdf field #### sdf = self.sdf_network.sdf(pts_exp).reshape(batch_size, self.n_samples) #### use the optimized sdf field #### for i in range(self.up_sample_steps): new_z_vals = self.up_sample(rays_o, rays_d, z_vals, sdf, self.n_importance // self.up_sample_steps, 64 * 2**i) z_vals, sdf = self.cat_z_vals(rays_o, rays_d, z_vals, new_z_vals, sdf, last=(i + 1 == self.up_sample_steps)) n_samples = self.n_samples + self.n_importance # Background model if self.n_outside > 0: z_vals_feed = torch.cat([z_vals, z_vals_outside], dim=-1) z_vals_feed, _ = torch.sort(z_vals_feed, dim=-1) ret_outside = self.render_core_outside(rays_o, rays_d, z_vals_feed, sample_dist, self.nerf) background_sampled_color = ret_outside['sampled_color'] background_alpha = ret_outside['alpha'] # Render core ret_fine = self.render_core(rays_o, # rays_d, z_vals, sample_dist, self.sdf_network, self.deviation_network, self.color_network, background_rgb=background_rgb, background_alpha=background_alpha, background_sampled_color=background_sampled_color, cos_anneal_ratio=cos_anneal_ratio) color_fine = ret_fine['color'] weights = ret_fine['weights'] weights_sum = weights.sum(dim=-1, keepdim=True) gradients = ret_fine['gradients'] s_val = ret_fine['s_val'].reshape(batch_size, n_samples).mean(dim=-1, keepdim=True) return { 'color_fine': color_fine, 's_val': s_val, 'cdf_fine': ret_fine['cdf'], 'weight_sum': weights_sum, 'weight_max': torch.max(weights, dim=-1, keepdim=True)[0], 'gradients': gradients, 'weights': weights, 'gradient_error': ret_fine['gradient_error'], 'inside_sphere': ret_fine['inside_sphere'] } def extract_geometry(self, bound_min, bound_max, resolution, threshold=0.0): return extract_geometry(bound_min, # extract geometry # bound_max, resolution=resolution, threshold=threshold, query_func=lambda pts: -self.sdf_network.sdf(pts)) def extract_geometry_tets(self, bound_min, bound_max, resolution, threshold=0.0): return extract_geometry_tets(bound_min, # extract geometry # bound_max, resolution=resolution, threshold=threshold, query_func=lambda pts: -self.sdf_network.sdf(pts))