# Save results
filename = f'3dmatch-stats_{method.__class__.__name__}'
if os.path.isdir(config.out_dir):
out_file = os.path.join(config.out_dir, filename)
else:
out_file = filename # save it on the current directory
print(f'Saving the stats to {out_file}')
np.savez("./result.txt", stats=stats, names=method_names)
analyze_stats(stats, mask, method_names)
if __name__ == '__main__':
config = get_config()
print(config)
dgr = DeepGlobalRegistration(config)
methods = [dgr]
method_names = ['ATOO']
path = "/data/xiaoshua/will/cross-source-dataset/"
dset = CrosssourceDataset(path)
data_loader = torch.utils.data.DataLoader(dset,
batch_size=1,
shuffle=False,
collate_fn=lambda x: x,
pin_memory=False,
drop_last=True)
evaluate(methods, method_names, data_loader, config, debug=True)
"ResUNetBN2C-feat32-3dmatch-v0.05.pth")]
# Check if the weights and file exist and download
if not os.path.isfile('redkitchen_000.ply'):
print('Downloading weights and pointcloud files...')
for f in DOWNLOAD_LIST:
print(f"Downloading {f}")
urlretrieve(f[0] + f[1], f[1])
if __name__ == '__main__':
config = get_config()
if config.weights is None:
config.weights = DOWNLOAD_LIST[-1][-1]
# preprocessing
pcd0 = o3d.io.read_point_cloud(config.pcd0)
pcd0.estimate_normals()
pcd1 = o3d.io.read_point_cloud(config.pcd1)
pcd1.estimate_normals()
# registration
dgr = DeepGlobalRegistration(config)
T01 = dgr.register(pcd0, pcd1)
o3d.visualization.draw_geometries([pcd0, pcd1])
pcd0.transform(T01)
print(T01)
o3d.visualization.draw_geometries([pcd0, pcd1])