import torch from torch import nn import numpy as np from tqdm import tqdm from matplotlib import pyplot as pl import mast3r.utils.path_to_dust3r # noqa from dust3r.utils.geometry import depthmap_to_pts3d, geotrf, inv class TSDFPostProcess: """ Optimizes a signed distance-function to improve depthmaps. """ def __init__(self, optimizer, subsample=8, TSDF_thresh=0., TSDF_batchsize=int(1e7)): self.TSDF_thresh = TSDF_thresh # None -> no TSDF self.TSDF_batchsize = TSDF_batchsize self.optimizer = optimizer pts3d, depthmaps, confs = optimizer.get_dense_pts3d(clean_depth=False, subsample=subsample) pts3d, depthmaps = self._TSDF_postprocess_or_not(pts3d, depthmaps, confs) self.pts3d = pts3d self.depthmaps = depthmaps self.confs = confs def _get_depthmaps(self, TSDF_filtering_thresh=None): if TSDF_filtering_thresh: self._refine_depths_with_TSDF(self.optimizer, TSDF_filtering_thresh) # compute refined depths if needed dms = self.TSDF_im_depthmaps if TSDF_filtering_thresh else self.im_depthmaps return [d.exp() for d in dms] @torch.no_grad() def _refine_depths_with_TSDF(self, TSDF_filtering_thresh, niter=1, nsamples=1000): """ Leverage TSDF to post-process estimated depths for each pixel, find zero level of TSDF along ray (or closest to 0) """ print("Post-Processing Depths with TSDF fusion.") self.TSDF_im_depthmaps = [] alldepths, allposes, allfocals, allpps, allimshapes = self._get_depthmaps(), self.optimizer.get_im_poses( ), self.optimizer.get_focals(), self.optimizer.get_principal_points(), self.imshapes for vi in tqdm(range(self.optimizer.n_imgs)): dm, pose, focal, pp, imshape = alldepths[vi], allposes[vi], allfocals[vi], allpps[vi], allimshapes[vi] minvals = torch.full(dm.shape, 1e20) for it in range(niter): H, W = dm.shape curthresh = (niter - it) * TSDF_filtering_thresh dm_offsets = (torch.randn(H, W, nsamples).to(dm) - 1.) * \ curthresh # decreasing search std along with iterations newdm = dm[..., None] + dm_offsets # [H,W,Nsamp] curproj = self._backproj_pts3d(in_depths=[newdm], in_im_poses=pose[None], in_focals=focal[None], in_pps=pp[None], in_imshapes=[ imshape])[0] # [H,W,Nsamp,3] # Batched TSDF eval curproj = curproj.view(-1, 3) tsdf_vals = [] valids = [] for batch in range(0, len(curproj), self.TSDF_batchsize): values, valid = self._TSDF_query( curproj[batch:min(batch + self.TSDF_batchsize, len(curproj))], curthresh) tsdf_vals.append(values) valids.append(valid) tsdf_vals = torch.cat(tsdf_vals, dim=0) valids = torch.cat(valids, dim=0) tsdf_vals = tsdf_vals.view([H, W, nsamples]) valids = valids.view([H, W, nsamples]) # keep depth value that got us the closest to 0 tsdf_vals[~valids] = torch.inf # ignore invalid values tsdf_vals = tsdf_vals.abs() mins = torch.argmin(tsdf_vals, dim=-1, keepdim=True) # when all samples live on a very flat zone, do nothing allbad = (tsdf_vals == curthresh).sum(dim=-1) == nsamples dm[~allbad] = torch.gather(newdm, -1, mins)[..., 0][~allbad] # Save refined depth map self.TSDF_im_depthmaps.append(dm.log()) def _TSDF_query(self, qpoints, TSDF_filtering_thresh, weighted=True): """ TSDF query call: returns the weighted TSDF value for each query point [N, 3] """ N, three = qpoints.shape assert three == 3 qpoints = qpoints[None].repeat(self.optimizer.n_imgs, 1, 1) # [B,N,3] # get projection coordinates and depths onto images coords_and_depth = self._proj_pts3d(pts3d=qpoints, cam2worlds=self.optimizer.get_im_poses( ), focals=self.optimizer.get_focals(), pps=self.optimizer.get_principal_points()) image_coords = coords_and_depth[..., :2].round().to(int) # for now, there's no interpolation... proj_depths = coords_and_depth[..., -1] # recover depth values after scene optim pred_depths, pred_confs, valids = self._get_pixel_depths(image_coords) # Gather TSDF scores all_SDF_scores = pred_depths - proj_depths # SDF unseen = all_SDF_scores < -TSDF_filtering_thresh # handle visibility # all_TSDF_scores = all_SDF_scores.clip(-TSDF_filtering_thresh,TSDF_filtering_thresh) # SDF -> TSDF all_TSDF_scores = all_SDF_scores.clip(-TSDF_filtering_thresh, 1e20) # SDF -> TSDF # Gather TSDF confidences and ignore points that are unseen, either OOB during reproj or too far behind seen depth all_TSDF_weights = (~unseen).float() * valids.float() if weighted: all_TSDF_weights = pred_confs.exp() * all_TSDF_weights # Aggregate all votes, ignoring zeros TSDF_weights = all_TSDF_weights.sum(dim=0) valids = TSDF_weights != 0. TSDF_wsum = (all_TSDF_weights * all_TSDF_scores).sum(dim=0) TSDF_wsum[valids] /= TSDF_weights[valids] return TSDF_wsum, valids def _get_pixel_depths(self, image_coords, TSDF_filtering_thresh=None, with_normals_conf=False): """ Recover depth value for each input pixel coordinate, along with OOB validity mask """ B, N, two = image_coords.shape assert B == self.optimizer.n_imgs and two == 2 depths = torch.zeros([B, N], device=image_coords.device) valids = torch.zeros([B, N], dtype=bool, device=image_coords.device) confs = torch.zeros([B, N], device=image_coords.device) curconfs = self._get_confs_with_normals() if with_normals_conf else self.im_conf for ni, (imc, depth, conf) in enumerate(zip(image_coords, self._get_depthmaps(TSDF_filtering_thresh), curconfs)): H, W = depth.shape valids[ni] = torch.logical_and(0 <= imc[:, 1], imc[:, 1] < H) & torch.logical_and(0 <= imc[:, 0], imc[:, 0] < W) imc[~valids[ni]] = 0 depths[ni] = depth[imc[:, 1], imc[:, 0]] confs[ni] = conf.cuda()[imc[:, 1], imc[:, 0]] return depths, confs, valids def _get_confs_with_normals(self): outconfs = [] # Confidence basedf on depth gradient class Sobel(nn.Module): def __init__(self): super().__init__() self.filter = nn.Conv2d(in_channels=1, out_channels=2, kernel_size=3, stride=1, padding=1, bias=False) Gx = torch.tensor([[2.0, 0.0, -2.0], [4.0, 0.0, -4.0], [2.0, 0.0, -2.0]]) Gy = torch.tensor([[2.0, 4.0, 2.0], [0.0, 0.0, 0.0], [-2.0, -4.0, -2.0]]) G = torch.cat([Gx.unsqueeze(0), Gy.unsqueeze(0)], 0) G = G.unsqueeze(1) self.filter.weight = nn.Parameter(G, requires_grad=False) def forward(self, img): x = self.filter(img) x = torch.mul(x, x) x = torch.sum(x, dim=1, keepdim=True) x = torch.sqrt(x) return x grad_op = Sobel().to(self.im_depthmaps[0].device) for conf, depth in zip(self.im_conf, self.im_depthmaps): grad_confs = (1. - grad_op(depth[None, None])[0, 0]).clip(0) if not 'dbg show': pl.imshow(grad_confs.cpu()) pl.show() outconfs.append(conf * grad_confs.to(conf)) return outconfs def _proj_pts3d(self, pts3d, cam2worlds, focals, pps): """ Projection operation: from 3D points to 2D coordinates + depths """ B = pts3d.shape[0] assert pts3d.shape[0] == cam2worlds.shape[0] # prepare Extrinsincs R, t = cam2worlds[:, :3, :3], cam2worlds[:, :3, -1] Rinv = R.transpose(-2, -1) tinv = -Rinv @ t[..., None] # prepare intrinsics intrinsics = torch.eye(3).to(cam2worlds)[None].repeat(focals.shape[0], 1, 1) if len(focals.shape) == 1: focals = torch.stack([focals, focals], dim=-1) intrinsics[:, 0, 0] = focals[:, 0] intrinsics[:, 1, 1] = focals[:, 1] intrinsics[:, :2, -1] = pps # Project projpts = intrinsics @ (Rinv @ pts3d.transpose(-2, -1) + tinv) # I(RX+t) : [B,3,N] projpts = projpts.transpose(-2, -1) # [B,N,3] projpts[..., :2] /= projpts[..., [-1]] # [B,N,3] (X/Z , Y/Z, Z) return projpts def _backproj_pts3d(self, in_depths=None, in_im_poses=None, in_focals=None, in_pps=None, in_imshapes=None): """ Backprojection operation: from image depths to 3D points """ # Get depths and projection params if not provided focals = self.optimizer.get_focals() if in_focals is None else in_focals im_poses = self.optimizer.get_im_poses() if in_im_poses is None else in_im_poses depth = self._get_depthmaps() if in_depths is None else in_depths pp = self.optimizer.get_principal_points() if in_pps is None else in_pps imshapes = self.imshapes if in_imshapes is None else in_imshapes def focal_ex(i): return focals[i][..., None, None].expand(1, *focals[i].shape, *imshapes[i]) dm_to_3d = [depthmap_to_pts3d(depth[i][None], focal_ex(i), pp=pp[[i]]) for i in range(im_poses.shape[0])] def autoprocess(x): x = x[0] return x.transpose(-2, -1) if len(x.shape) == 4 else x return [geotrf(pose, autoprocess(pt)) for pose, pt in zip(im_poses, dm_to_3d)] def _pts3d_to_depth(self, pts3d, cam2worlds, focals, pps): """ Projection operation: from 3D points to 2D coordinates + depths """ B = pts3d.shape[0] assert pts3d.shape[0] == cam2worlds.shape[0] # prepare Extrinsincs R, t = cam2worlds[:, :3, :3], cam2worlds[:, :3, -1] Rinv = R.transpose(-2, -1) tinv = -Rinv @ t[..., None] # prepare intrinsics intrinsics = torch.eye(3).to(cam2worlds)[None].repeat(self.optimizer.n_imgs, 1, 1) if len(focals.shape) == 1: focals = torch.stack([focals, focals], dim=-1) intrinsics[:, 0, 0] = focals[:, 0] intrinsics[:, 1, 1] = focals[:, 1] intrinsics[:, :2, -1] = pps # Project projpts = intrinsics @ (Rinv @ pts3d.transpose(-2, -1) + tinv) # I(RX+t) : [B,3,N] projpts = projpts.transpose(-2, -1) # [B,N,3] projpts[..., :2] /= projpts[..., [-1]] # [B,N,3] (X/Z , Y/Z, Z) return projpts def _depth_to_pts3d(self, in_depths=None, in_im_poses=None, in_focals=None, in_pps=None, in_imshapes=None): """ Backprojection operation: from image depths to 3D points """ # Get depths and projection params if not provided focals = self.optimizer.get_focals() if in_focals is None else in_focals im_poses = self.optimizer.get_im_poses() if in_im_poses is None else in_im_poses depth = self._get_depthmaps() if in_depths is None else in_depths pp = self.optimizer.get_principal_points() if in_pps is None else in_pps imshapes = self.imshapes if in_imshapes is None else in_imshapes def focal_ex(i): return focals[i][..., None, None].expand(1, *focals[i].shape, *imshapes[i]) dm_to_3d = [depthmap_to_pts3d(depth[i][None], focal_ex(i), pp=pp[i:i + 1]) for i in range(im_poses.shape[0])] def autoprocess(x): x = x[0] H, W, three = x.shape[:3] return x.transpose(-2, -1) if len(x.shape) == 4 else x return [geotrf(pp, autoprocess(pt)) for pp, pt in zip(im_poses, dm_to_3d)] def _get_pts3d(self, TSDF_filtering_thresh=None, **kw): """ return 3D points (possibly filtering depths with TSDF) """ return self._backproj_pts3d(in_depths=self._get_depthmaps(TSDF_filtering_thresh=TSDF_filtering_thresh), **kw) def _TSDF_postprocess_or_not(self, pts3d, depthmaps, confs, niter=1): # Setup inner variables self.imshapes = [im.shape[:2] for im in self.optimizer.imgs] self.im_depthmaps = [dd.log().view(imshape) for dd, imshape in zip(depthmaps, self.imshapes)] self.im_conf = confs if self.TSDF_thresh > 0.: # Create or update self.TSDF_im_depthmaps that contain logdepths filtered with TSDF self._refine_depths_with_TSDF(self.TSDF_thresh, niter=niter) depthmaps = [dd.exp() for dd in self.TSDF_im_depthmaps] # Turn them into 3D points pts3d = self._backproj_pts3d(in_depths=depthmaps) depthmaps = [dd.flatten() for dd in depthmaps] pts3d = [pp.view(-1, 3) for pp in pts3d] return pts3d, depthmaps def get_dense_pts3d(self, clean_depth=True): return self.pts3d, self.depthmaps, self.confs