# 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()