# Copyright (C) 2024-present Naver Corporation. All rights reserved. # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). # # -------------------------------------------------------- # Slower implementation of the global alignment that allows to freeze partial poses/intrinsics # -------------------------------------------------------- import numpy as np import torch import torch.nn as nn from dust3r.cloud_opt.base_opt import BasePCOptimizer from dust3r.utils.geometry import geotrf from dust3r.utils.device import to_cpu, to_numpy from dust3r.utils.geometry import depthmap_to_pts3d class ModularPointCloudOptimizer (BasePCOptimizer): """ Optimize a global scene, given a list of pairwise observations. Unlike PointCloudOptimizer, you can fix parts of the optimization process (partial poses/intrinsics) Graph node: images Graph edges: observations = (pred1, pred2) """ def __init__(self, *args, optimize_pp=False, fx_and_fy=False, focal_brake=20, **kwargs): super().__init__(*args, **kwargs) self.has_im_poses = True # by definition of this class self.focal_brake = focal_brake # adding thing to optimize self.im_depthmaps = nn.ParameterList(torch.randn(H, W)/10-3 for H, W in self.imshapes) # log(depth) self.im_poses = nn.ParameterList(self.rand_pose(self.POSE_DIM) for _ in range(self.n_imgs)) # camera poses default_focals = [self.focal_brake * np.log(max(H, W)) for H, W in self.imshapes] self.im_focals = nn.ParameterList(torch.FloatTensor([f, f] if fx_and_fy else [ f]) for f in default_focals) # camera intrinsics self.im_pp = nn.ParameterList(torch.zeros((2,)) for _ in range(self.n_imgs)) # camera intrinsics self.im_pp.requires_grad_(optimize_pp) def preset_pose(self, known_poses, pose_msk=None): # cam-to-world if isinstance(known_poses, torch.Tensor) and known_poses.ndim == 2: known_poses = [known_poses] for idx, pose in zip(self._get_msk_indices(pose_msk), known_poses): if self.verbose: print(f' (setting pose #{idx} = {pose[:3,3]})') self._no_grad(self._set_pose(self.im_poses, idx, torch.tensor(pose), force=True)) # normalize scale if there's less than 1 known pose n_known_poses = sum((p.requires_grad is False) for p in self.im_poses) self.norm_pw_scale = (n_known_poses <= 1) def preset_intrinsics(self, known_intrinsics, msk=None): if isinstance(known_intrinsics, torch.Tensor) and known_intrinsics.ndim == 2: known_intrinsics = [known_intrinsics] for K in known_intrinsics: assert K.shape == (3, 3) self.preset_focal([K.diagonal()[:2].mean() for K in known_intrinsics], msk) self.preset_principal_point([K[:2, 2] for K in known_intrinsics], msk) def preset_focal(self, known_focals, msk=None): for idx, focal in zip(self._get_msk_indices(msk), known_focals): if self.verbose: print(f' (setting focal #{idx} = {focal})') self._no_grad(self._set_focal(idx, focal, force=True)) def preset_principal_point(self, known_pp, msk=None): for idx, pp in zip(self._get_msk_indices(msk), known_pp): if self.verbose: print(f' (setting principal point #{idx} = {pp})') self._no_grad(self._set_principal_point(idx, pp, force=True)) def _no_grad(self, tensor): return tensor.requires_grad_(False) def _get_msk_indices(self, msk): if msk is None: return range(self.n_imgs) elif isinstance(msk, int): return [msk] elif isinstance(msk, (tuple, list)): return self._get_msk_indices(np.array(msk)) elif msk.dtype in (bool, torch.bool, np.bool_): assert len(msk) == self.n_imgs return np.where(msk)[0] elif np.issubdtype(msk.dtype, np.integer): return msk else: raise ValueError(f'bad {msk=}') def _set_focal(self, idx, focal, force=False): param = self.im_focals[idx] if param.requires_grad or force: # can only init a parameter not already initialized param.data[:] = self.focal_brake * np.log(focal) return param def get_focals(self): log_focals = torch.stack(list(self.im_focals), dim=0) return (log_focals / self.focal_brake).exp() def _set_principal_point(self, idx, pp, force=False): param = self.im_pp[idx] H, W = self.imshapes[idx] if param.requires_grad or force: # can only init a parameter not already initialized param.data[:] = to_cpu(to_numpy(pp) - (W/2, H/2)) / 10 return param def get_principal_points(self): return torch.stack([pp.new((W/2, H/2))+10*pp for pp, (H, W) in zip(self.im_pp, self.imshapes)]) def get_intrinsics(self): K = torch.zeros((self.n_imgs, 3, 3), device=self.device) focals = self.get_focals().view(self.n_imgs, -1) K[:, 0, 0] = focals[:, 0] K[:, 1, 1] = focals[:, -1] K[:, :2, 2] = self.get_principal_points() K[:, 2, 2] = 1 return K def get_im_poses(self): # cam to world cam2world = self._get_poses(torch.stack(list(self.im_poses))) return cam2world def _set_depthmap(self, idx, depth, force=False): param = self.im_depthmaps[idx] if param.requires_grad or force: # can only init a parameter not already initialized param.data[:] = depth.log().nan_to_num(neginf=0) return param def get_depthmaps(self): return [d.exp() for d in self.im_depthmaps] def depth_to_pts3d(self): # Get depths and projection params if not provided focals = self.get_focals() pp = self.get_principal_points() im_poses = self.get_im_poses() depth = self.get_depthmaps() # convert focal to (1,2,H,W) constant field def focal_ex(i): return focals[i][..., None, None].expand(1, *focals[i].shape, *self.imshapes[i]) # get pointmaps in camera frame rel_ptmaps = [depthmap_to_pts3d(depth[i][None], focal_ex(i), pp=pp[i:i+1])[0] for i in range(im_poses.shape[0])] # project to world frame return [geotrf(pose, ptmap) for pose, ptmap in zip(im_poses, rel_ptmaps)] def get_pts3d(self): return self.depth_to_pts3d()