Image-Matching-app / tsdf_optimizer.py
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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
from dust3r.cloud_opt.base_opt import clean_pointcloud
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):
if clean_depth:
confs = clean_pointcloud(self.confs, self.optimizer.intrinsics, inv(self.optimizer.cam2w),
self.depthmaps, self.pts3d)
return self.pts3d, self.depthmaps, confs