import torch import torch.nn.functional as F import torch.nn as nn from icecream import ic # - neus: use sphere-tracing to speed up depth maps extraction # This code snippet is heavily borrowed from IDR. class FastRenderer(nn.Module): def __init__(self): super(FastRenderer, self).__init__() self.sdf_threshold = 5e-5 self.line_search_step = 0.5 self.line_step_iters = 1 self.sphere_tracing_iters = 10 self.n_steps = 100 self.n_secant_steps = 8 # - use sdf_network to inference sdf value or directly interpolate sdf value from precomputed sdf_volume self.network_inference = False def extract_depth_maps(self, rays_o, rays_d, near, far, sdf_network, conditional_volume): with torch.no_grad(): curr_start_points, network_object_mask, acc_start_dis = self.get_intersection( rays_o, rays_d, near, far, sdf_network, conditional_volume) network_object_mask = network_object_mask.reshape(-1) return network_object_mask, acc_start_dis def get_intersection(self, rays_o, rays_d, near, far, sdf_network, conditional_volume): device = rays_o.device num_pixels, _ = rays_d.shape curr_start_points, unfinished_mask_start, acc_start_dis, acc_end_dis, min_dis, max_dis = \ self.sphere_tracing(rays_o, rays_d, near, far, sdf_network, conditional_volume) network_object_mask = (acc_start_dis < acc_end_dis) # The non convergent rays should be handled by the sampler sampler_mask = unfinished_mask_start sampler_net_obj_mask = torch.zeros_like(sampler_mask).bool().to(device) if sampler_mask.sum() > 0: # sampler_min_max = torch.zeros((num_pixels, 2)).to(device) # sampler_min_max[sampler_mask, 0] = acc_start_dis[sampler_mask] # sampler_min_max[sampler_mask, 1] = acc_end_dis[sampler_mask] # ray_sampler(self, rays_o, rays_d, near, far, sampler_mask): sampler_pts, sampler_net_obj_mask, sampler_dists = self.ray_sampler(rays_o, rays_d, acc_start_dis, acc_end_dis, sampler_mask, sdf_network, conditional_volume ) curr_start_points[sampler_mask] = sampler_pts[sampler_mask] acc_start_dis[sampler_mask] = sampler_dists[sampler_mask][:, None] network_object_mask[sampler_mask] = sampler_net_obj_mask[sampler_mask][:, None] # print('----------------------------------------------------------------') # print('RayTracing: object = {0}/{1}, secant on {2}/{3}.' # .format(network_object_mask.sum(), len(network_object_mask), sampler_net_obj_mask.sum(), # sampler_mask.sum())) # print('----------------------------------------------------------------') return curr_start_points, network_object_mask, acc_start_dis def sphere_tracing(self, rays_o, rays_d, near, far, sdf_network, conditional_volume): ''' Run sphere tracing algorithm for max iterations from both sides of unit sphere intersection ''' device = rays_o.device unfinished_mask_start = (near < far).reshape(-1).clone() unfinished_mask_end = (near < far).reshape(-1).clone() # Initialize start current points curr_start_points = rays_o + rays_d * near acc_start_dis = near.clone() # Initialize end current points curr_end_points = rays_o + rays_d * far acc_end_dis = far.clone() # Initizlize min and max depth min_dis = acc_start_dis.clone() max_dis = acc_end_dis.clone() # Iterate on the rays (from both sides) till finding a surface iters = 0 next_sdf_start = torch.zeros_like(acc_start_dis).to(device) if self.network_inference: sdf_func = sdf_network.sdf else: sdf_func = sdf_network.sdf_from_sdfvolume next_sdf_start[unfinished_mask_start] = sdf_func( curr_start_points[unfinished_mask_start], conditional_volume, lod=0, gru_fusion=False)['sdf_pts_scale%d' % 0] next_sdf_end = torch.zeros_like(acc_end_dis).to(device) next_sdf_end[unfinished_mask_end] = sdf_func(curr_end_points[unfinished_mask_end], conditional_volume, lod=0, gru_fusion=False)[ 'sdf_pts_scale%d' % 0] while True: # Update sdf curr_sdf_start = torch.zeros_like(acc_start_dis).to(device) curr_sdf_start[unfinished_mask_start] = next_sdf_start[unfinished_mask_start] curr_sdf_start[curr_sdf_start <= self.sdf_threshold] = 0 curr_sdf_end = torch.zeros_like(acc_end_dis).to(device) curr_sdf_end[unfinished_mask_end] = next_sdf_end[unfinished_mask_end] curr_sdf_end[curr_sdf_end <= self.sdf_threshold] = 0 # Update masks unfinished_mask_start = unfinished_mask_start & (curr_sdf_start > self.sdf_threshold).reshape(-1) unfinished_mask_end = unfinished_mask_end & (curr_sdf_end > self.sdf_threshold).reshape(-1) if ( unfinished_mask_start.sum() == 0 and unfinished_mask_end.sum() == 0) or iters == self.sphere_tracing_iters: break iters += 1 # Make step # Update distance acc_start_dis = acc_start_dis + curr_sdf_start acc_end_dis = acc_end_dis - curr_sdf_end # Update points curr_start_points = rays_o + acc_start_dis * rays_d curr_end_points = rays_o + acc_end_dis * rays_d # Fix points which wrongly crossed the surface next_sdf_start = torch.zeros_like(acc_start_dis).to(device) if unfinished_mask_start.sum() > 0: next_sdf_start[unfinished_mask_start] = sdf_func(curr_start_points[unfinished_mask_start], conditional_volume, lod=0, gru_fusion=False)[ 'sdf_pts_scale%d' % 0] next_sdf_end = torch.zeros_like(acc_end_dis).to(device) if unfinished_mask_end.sum() > 0: next_sdf_end[unfinished_mask_end] = sdf_func(curr_end_points[unfinished_mask_end], conditional_volume, lod=0, gru_fusion=False)[ 'sdf_pts_scale%d' % 0] not_projected_start = (next_sdf_start < 0).reshape(-1) not_projected_end = (next_sdf_end < 0).reshape(-1) not_proj_iters = 0 while ( not_projected_start.sum() > 0 or not_projected_end.sum() > 0) and not_proj_iters < self.line_step_iters: # Step backwards if not_projected_start.sum() > 0: acc_start_dis[not_projected_start] -= ((1 - self.line_search_step) / (2 ** not_proj_iters)) * \ curr_sdf_start[not_projected_start] curr_start_points[not_projected_start] = (rays_o + acc_start_dis * rays_d)[not_projected_start] next_sdf_start[not_projected_start] = sdf_func( curr_start_points[not_projected_start], conditional_volume, lod=0, gru_fusion=False)['sdf_pts_scale%d' % 0] if not_projected_end.sum() > 0: acc_end_dis[not_projected_end] += ((1 - self.line_search_step) / (2 ** not_proj_iters)) * \ curr_sdf_end[ not_projected_end] curr_end_points[not_projected_end] = (rays_o + acc_end_dis * rays_d)[not_projected_end] # Calc sdf next_sdf_end[not_projected_end] = sdf_func( curr_end_points[not_projected_end], conditional_volume, lod=0, gru_fusion=False)['sdf_pts_scale%d' % 0] # Update mask not_projected_start = (next_sdf_start < 0).reshape(-1) not_projected_end = (next_sdf_end < 0).reshape(-1) not_proj_iters += 1 unfinished_mask_start = unfinished_mask_start & (acc_start_dis < acc_end_dis).reshape(-1) unfinished_mask_end = unfinished_mask_end & (acc_start_dis < acc_end_dis).reshape(-1) return curr_start_points, unfinished_mask_start, acc_start_dis, acc_end_dis, min_dis, max_dis def ray_sampler(self, rays_o, rays_d, near, far, sampler_mask, sdf_network, conditional_volume): ''' Sample the ray in a given range and run secant on rays which have sign transition ''' device = rays_o.device num_pixels, _ = rays_d.shape sampler_pts = torch.zeros(num_pixels, 3).to(device).float() sampler_dists = torch.zeros(num_pixels).to(device).float() intervals_dist = torch.linspace(0, 1, steps=self.n_steps).to(device).view(1, -1) pts_intervals = near + intervals_dist * (far - near) points = rays_o[:, None, :] + pts_intervals[:, :, None] * rays_d[:, None, :] # Get the non convergent rays mask_intersect_idx = torch.nonzero(sampler_mask).flatten() points = points.reshape((-1, self.n_steps, 3))[sampler_mask, :, :] pts_intervals = pts_intervals.reshape((-1, self.n_steps))[sampler_mask] if self.network_inference: sdf_func = sdf_network.sdf else: sdf_func = sdf_network.sdf_from_sdfvolume sdf_val_all = [] for pnts in torch.split(points.reshape(-1, 3), 100000, dim=0): sdf_val_all.append(sdf_func(pnts, conditional_volume, lod=0, gru_fusion=False)['sdf_pts_scale%d' % 0]) sdf_val = torch.cat(sdf_val_all).reshape(-1, self.n_steps) tmp = torch.sign(sdf_val) * torch.arange(self.n_steps, 0, -1).to(device).float().reshape( (1, self.n_steps)) # Force argmin to return the first min value sampler_pts_ind = torch.argmin(tmp, -1) sampler_pts[mask_intersect_idx] = points[torch.arange(points.shape[0]), sampler_pts_ind, :] sampler_dists[mask_intersect_idx] = pts_intervals[torch.arange(pts_intervals.shape[0]), sampler_pts_ind] net_surface_pts = (sdf_val[torch.arange(sdf_val.shape[0]), sampler_pts_ind] < 0) # take points with minimal SDF value for P_out pixels p_out_mask = ~net_surface_pts n_p_out = p_out_mask.sum() if n_p_out > 0: out_pts_idx = torch.argmin(sdf_val[p_out_mask, :], -1) sampler_pts[mask_intersect_idx[p_out_mask]] = points[p_out_mask, :, :][torch.arange(n_p_out), out_pts_idx, :] sampler_dists[mask_intersect_idx[p_out_mask]] = pts_intervals[p_out_mask, :][ torch.arange(n_p_out), out_pts_idx] # Get Network object mask sampler_net_obj_mask = sampler_mask.clone() sampler_net_obj_mask[mask_intersect_idx[~net_surface_pts]] = False # Run Secant method secant_pts = net_surface_pts n_secant_pts = secant_pts.sum() if n_secant_pts > 0: # Get secant z predictions z_high = pts_intervals[torch.arange(pts_intervals.shape[0]), sampler_pts_ind][secant_pts] sdf_high = sdf_val[torch.arange(sdf_val.shape[0]), sampler_pts_ind][secant_pts] z_low = pts_intervals[secant_pts][torch.arange(n_secant_pts), sampler_pts_ind[secant_pts] - 1] sdf_low = sdf_val[secant_pts][torch.arange(n_secant_pts), sampler_pts_ind[secant_pts] - 1] cam_loc_secant = rays_o[mask_intersect_idx[secant_pts]] ray_directions_secant = rays_d[mask_intersect_idx[secant_pts]] z_pred_secant = self.secant(sdf_low, sdf_high, z_low, z_high, cam_loc_secant, ray_directions_secant, sdf_network, conditional_volume) # Get points sampler_pts[mask_intersect_idx[secant_pts]] = cam_loc_secant + z_pred_secant[:, None] * ray_directions_secant sampler_dists[mask_intersect_idx[secant_pts]] = z_pred_secant return sampler_pts, sampler_net_obj_mask, sampler_dists def secant(self, sdf_low, sdf_high, z_low, z_high, rays_o, rays_d, sdf_network, conditional_volume): ''' Runs the secant method for interval [z_low, z_high] for n_secant_steps ''' if self.network_inference: sdf_func = sdf_network.sdf else: sdf_func = sdf_network.sdf_from_sdfvolume z_pred = -sdf_low * (z_high - z_low) / (sdf_high - sdf_low) + z_low for i in range(self.n_secant_steps): p_mid = rays_o + z_pred[:, None] * rays_d sdf_mid = sdf_func(p_mid, conditional_volume, lod=0, gru_fusion=False)['sdf_pts_scale%d' % 0].reshape(-1) ind_low = (sdf_mid > 0).reshape(-1) if ind_low.sum() > 0: z_low[ind_low] = z_pred[ind_low] sdf_low[ind_low] = sdf_mid[ind_low] ind_high = sdf_mid < 0 if ind_high.sum() > 0: z_high[ind_high] = z_pred[ind_high] sdf_high[ind_high] = sdf_mid[ind_high] z_pred = - sdf_low * (z_high - z_low) / (sdf_high - sdf_low) + z_low return z_pred # 1D tensor def minimal_sdf_points(self, num_pixels, sdf, cam_loc, ray_directions, mask, min_dis, max_dis): ''' Find points with minimal SDF value on rays for P_out pixels ''' device = sdf.device n_mask_points = mask.sum() n = self.n_steps # steps = torch.linspace(0.0, 1.0,n).to(device) steps = torch.empty(n).uniform_(0.0, 1.0).to(device) mask_max_dis = max_dis[mask].unsqueeze(-1) mask_min_dis = min_dis[mask].unsqueeze(-1) steps = steps.unsqueeze(0).repeat(n_mask_points, 1) * (mask_max_dis - mask_min_dis) + mask_min_dis mask_points = cam_loc.unsqueeze(1).repeat(1, num_pixels, 1).reshape(-1, 3)[mask] mask_rays = ray_directions[mask, :] mask_points_all = mask_points.unsqueeze(1).repeat(1, n, 1) + steps.unsqueeze(-1) * mask_rays.unsqueeze( 1).repeat(1, n, 1) points = mask_points_all.reshape(-1, 3) mask_sdf_all = [] for pnts in torch.split(points, 100000, dim=0): mask_sdf_all.append(sdf(pnts)) mask_sdf_all = torch.cat(mask_sdf_all).reshape(-1, n) min_vals, min_idx = mask_sdf_all.min(-1) min_mask_points = mask_points_all.reshape(-1, n, 3)[torch.arange(0, n_mask_points), min_idx] min_mask_dist = steps.reshape(-1, n)[torch.arange(0, n_mask_points), min_idx] return min_mask_points, min_mask_dist