/
threedmatch_loader.py
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/
threedmatch_loader.py
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# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu) and Wei Dong (weidong@andrew.cmu.edu)
#
# Please cite the following papers if you use any part of the code.
# - Christopher Choy, Wei Dong, Vladlen Koltun, Deep Global Registration, CVPR 2020
# - Christopher Choy, Jaesik Park, Vladlen Koltun, Fully Convolutional Geometric Features, ICCV 2019
# - Christopher Choy, JunYoung Gwak, Silvio Savarese, 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks, CVPR 2019
import glob
from dataloader.base_loader import *
from dataloader.transforms import *
from util.pointcloud import get_matching_indices, make_open3d_point_cloud
from util.file import read_trajectory
class IndoorPairDataset(PairDataset):
'''
Train dataset
'''
OVERLAP_RATIO = None
AUGMENT = None
def __init__(self,
phase,
transform=None,
random_rotation=True,
random_scale=True,
manual_seed=False,
config=None):
PairDataset.__init__(self, phase, transform, random_rotation, random_scale,
manual_seed, config)
self.root = root = config.threed_match_dir
self.use_xyz_feature = config.use_xyz_feature
logging.info(f"Loading the subset {phase} from {root}")
subset_names = open(self.DATA_FILES[phase]).read().split()
for name in subset_names:
fname = name + "*%.2f.txt" % self.OVERLAP_RATIO
fnames_txt = glob.glob(root + "/" + fname)
assert len(fnames_txt) > 0, f"Make sure that the path {root} has data {fname}"
for fname_txt in fnames_txt:
with open(fname_txt) as f:
content = f.readlines()
fnames = [x.strip().split() for x in content]
for fname in fnames:
self.files.append([fname[0], fname[1]])
def __getitem__(self, idx):
file0 = os.path.join(self.root, self.files[idx][0])
file1 = os.path.join(self.root, self.files[idx][1])
data0 = np.load(file0)
data1 = np.load(file1)
xyz0 = data0["pcd"]
xyz1 = data1["pcd"]
matching_search_voxel_size = self.matching_search_voxel_size
if self.random_scale and random.random() < 0.95:
scale = self.min_scale + \
(self.max_scale - self.min_scale) * random.random()
matching_search_voxel_size *= scale
xyz0 = scale * xyz0
xyz1 = scale * xyz1
if self.random_rotation:
T0 = sample_random_trans(xyz0, self.randg, self.rotation_range)
T1 = sample_random_trans(xyz1, self.randg, self.rotation_range)
trans = T1 @ np.linalg.inv(T0)
xyz0 = self.apply_transform(xyz0, T0)
xyz1 = self.apply_transform(xyz1, T1)
else:
trans = np.identity(4)
# Voxelization
xyz0_th = torch.from_numpy(xyz0)
xyz1_th = torch.from_numpy(xyz1)
sel0 = ME.utils.sparse_quantize(xyz0_th / self.voxel_size, return_index=True)
sel1 = ME.utils.sparse_quantize(xyz1_th / self.voxel_size, return_index=True)
# Make point clouds using voxelized points
pcd0 = make_open3d_point_cloud(xyz0[sel0])
pcd1 = make_open3d_point_cloud(xyz1[sel1])
# Select features and points using the returned voxelized indices
# 3DMatch color is not helpful
# pcd0.colors = o3d.utility.Vector3dVector(color0[sel0])
# pcd1.colors = o3d.utility.Vector3dVector(color1[sel1])
# Get matches
matches = get_matching_indices(pcd0, pcd1, trans, matching_search_voxel_size)
# Get features
npts0 = len(sel0)
npts1 = len(sel1)
feats_train0, feats_train1 = [], []
unique_xyz0_th = xyz0_th[sel0]
unique_xyz1_th = xyz1_th[sel1]
# xyz as feats
if self.use_xyz_feature:
feats_train0.append(unique_xyz0_th - unique_xyz0_th.mean(0))
feats_train1.append(unique_xyz1_th - unique_xyz1_th.mean(0))
else:
feats_train0.append(torch.ones((npts0, 1)))
feats_train1.append(torch.ones((npts1, 1)))
feats0 = torch.cat(feats_train0, 1)
feats1 = torch.cat(feats_train1, 1)
coords0 = torch.floor(unique_xyz0_th / self.voxel_size)
coords1 = torch.floor(unique_xyz1_th / self.voxel_size)
if self.transform:
coords0, feats0 = self.transform(coords0, feats0)
coords1, feats1 = self.transform(coords1, feats1)
extra_package = {'idx': idx, 'file0': file0, 'file1': file1}
return (unique_xyz0_th.float(),
unique_xyz1_th.float(), coords0.int(), coords1.int(), feats0.float(),
feats1.float(), matches, trans, extra_package)
class ThreeDMatchPairDataset03(IndoorPairDataset):
OVERLAP_RATIO = 0.3
DATA_FILES = {
'train': './dataloader/split/train_3dmatch.txt',
'val': './dataloader/split/val_3dmatch.txt',
'test': './dataloader/split/test_3dmatch.txt'
}
class ThreeDMatchPairDataset05(ThreeDMatchPairDataset03):
OVERLAP_RATIO = 0.5
class ThreeDMatchPairDataset07(ThreeDMatchPairDataset03):
OVERLAP_RATIO = 0.7
class ThreeDMatchTrajectoryDataset(PairDataset):
'''
Test dataset
'''
DATA_FILES = {
'train': './dataloader/split/train_3dmatch.txt',
'val': './dataloader/split/val_3dmatch.txt',
'test': './dataloader/split/test_3dmatch.txt'
}
def __init__(self,
phase,
transform=None,
random_rotation=True,
random_scale=True,
manual_seed=False,
scene_id=None,
config=None,
return_ply_names=False):
PairDataset.__init__(self, phase, transform, random_rotation, random_scale,
manual_seed, config)
self.root = config.threed_match_dir
subset_names = open(self.DATA_FILES[phase]).read().split()
if scene_id is not None:
subset_names = [subset_names[scene_id]]
for sname in subset_names:
traj_file = os.path.join(self.root, sname + '-evaluation/gt.log')
assert os.path.exists(traj_file)
traj = read_trajectory(traj_file)
for ctraj in traj:
i = ctraj.metadata[0]
j = ctraj.metadata[1]
T_gt = ctraj.pose
self.files.append((sname, i, j, T_gt))
self.return_ply_names = return_ply_names
def __getitem__(self, pair_index):
sname, i, j, T_gt = self.files[pair_index]
ply_name0 = os.path.join(self.root, sname, f'cloud_bin_{i}.ply')
ply_name1 = os.path.join(self.root, sname, f'cloud_bin_{j}.ply')
if self.return_ply_names:
return sname, ply_name0, ply_name1, T_gt
pcd0 = o3d.io.read_point_cloud(ply_name0)
pcd1 = o3d.io.read_point_cloud(ply_name1)
pcd0 = np.asarray(pcd0.points)
pcd1 = np.asarray(pcd1.points)
return sname, pcd0, pcd1, T_gt