def solve(datArray, prm):
global scnFtArray, scnPcArray, Param
Param.update(prm)
scnFtArray = []
scnPcArray = []
for dat in datArray:
pc = fromNumpy(dat)
scnPcArray.append(pc)
scnFtArray.append(_get_features(pc))
t1 = time.time()
resft = o3.registration_ransac_based_on_feature_matching(
modPcArray[0], scnPcArray[0], modFtArray[0], scnFtArray[0],
Param["distance_threshold"],
o3.TransformationEstimationPointToPoint(False), 4, [
o3.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3.CorrespondenceCheckerBasedOnDistance(
Param["distance_threshold"])
], o3.RANSACConvergenceCriteria(2000000, 500))
print "time for feature matching", time.time() - t1
print "feature matching", resft.transformation, resft.fitness
resicp = o3.registration_icp(modPcArray[0], scnPcArray[0],
Param["icp_threshold"], resft.transformation,
o3.TransformationEstimationPointToPlane())
return {
"transform": [resicp.transformation],
"fitness": [resicp.fitness],
"rmse": [resicp.inlier_rmse]
}
def execute_global_registration(source_down: op3.PointCloud,
target_down: op3.PointCloud,
source_fpfh: op3.PointCloud,
target_fpfh: op3.PointCloud,
voxel_size: float,
verbose=False):
"""
Do global matching, find gross transformation form source to target
:param source_down, target_down: 2 objects of Open3D, that are point clouds of source and target after down-sampling
:param source_fpfh, target_fpfh: 2 objects of Open3D, that are point cloud features of source and target
:param voxel_size: a float value, that is how sparse you want the sample points is
:param verbose: a boolean value, display notification and visualization when True and no notification when False
:return: a transformation object
"""
distance_threshold = voxel_size * 1.5
if verbose:
print(":: RANSAC registration on downsampled point clouds.")
print(" Since the downsampling voxel size is %.3f," % voxel_size)
print(" we use a liberal distance threshold %.3f." %
distance_threshold)
result = op3.registration_ransac_based_on_feature_matching(
source_down, target_down, source_fpfh, target_fpfh, distance_threshold,
op3.TransformationEstimationPointToPoint(False), 4, [
op3.CorrespondenceCheckerBasedOnEdgeLength(0.9),
op3.CorrespondenceCheckerBasedOnDistance(distance_threshold)
], op3.RANSACConvergenceCriteria(4000000, 500))
return result
def execute_global_registration(src_keypts, tgt_keypts, src_desc, tgt_desc, distance_threshold):
result = open3d.registration_ransac_based_on_feature_matching(
src_keypts, tgt_keypts, src_desc, tgt_desc,
distance_threshold,
open3d.TransformationEstimationPointToPoint(False), 4,
[open3d.CorrespondenceCheckerBasedOnEdgeLength(0.9),
open3d.CorrespondenceCheckerBasedOnDistance(distance_threshold)],
open3d.RANSACConvergenceCriteria(4000000, 500))
return result
def execute_global_registration(kp1, kp2, fpfh1, fpfh2, voxel_size):
distance_threshold = voxel_size * 2.5
result = open3d.registration_ransac_based_on_feature_matching(
kp1, kp2, fpfh1, fpfh2, distance_threshold,
open3d.TransformationEstimationPointToPoint(False), 4, [
open3d.CorrespondenceCheckerBasedOnEdgeLength(0.9),
open3d.CorrespondenceCheckerBasedOnDistance(distance_threshold)
], open3d.RANSACConvergenceCriteria(500000, 1000))
return result
def match_():
source = copy.deepcopy(modPcArray[0])
source_fpfh = modFtArray[0]
target = scnPcArray[0]
target_fpfh = scnFtArray[0]
reg_global = o3d.registration_ransac_based_on_feature_matching(
source, target, source_fpfh, target_fpfh, param['distance_threshold'],
o3d.TransformationEstimationPointToPoint(False), 4, [
o3d.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3d.CorrespondenceCheckerBasedOnDistance(
param['distance_threshold'])
], o3d.RANSACConvergenceCriteria(4000000, 500))
source.transform(reg_global.transformation)
return reg_global, [source]
def icp_o3_gicp(file_idx,
cfg,
refine=None,
refine_radius=0.05,
precomputed_results=None):
pc1, pc2, pc1_centroid = load_pountclouds(file_idx, cfg)
voxel_size = 0.05
start = time.time()
if precomputed_results is None:
distance_threshold = voxel_size * 1.5
source_down, target_down, source_fpfh, target_fpfh = ICP._icp_global_prepare_dataset(
pc1, pc2, voxel_size)
reg_res = o3.registration_ransac_based_on_feature_matching(
source_down,
target_down,
source_fpfh,
target_fpfh,
distance_threshold,
o3.TransformationEstimationPointToPoint(
with_constraint=cfg.evaluation.special.icp.with_constraint,
with_scaling=False),
4, # scaling=False
[
o3.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3.CorrespondenceCheckerBasedOnDistance(distance_threshold)
],
o3.RANSACConvergenceCriteria(4000000, 500))
transformation = reg_res.transformation
else:
precomp_pred_translation, precomp_pred_angle, precomp_pred_center = precomputed_results
transformation = get_mat_angle(precomp_pred_translation,
precomp_pred_angle, precomp_pred_center)
if refine is None:
time_elapsed = time.time() - start
return transformation, pc1_centroid, time_elapsed
else:
if refine == 'p2p':
reg_p2p = o3.registration_icp(
pc1, pc2, refine_radius, transformation,
o3.TransformationEstimationPointToPoint(
with_constraint=cfg.evaluation.special.icp.with_constraint,
with_scaling=False))
# if file_idx == 8019:
# print('->', reg_p2p.transformation)
time_elapsed = time.time() - start
return reg_p2p.transformation, pc1_centroid, time_elapsed
else:
assert False
def execute_global_registration(source_down, target_down, source_fpfh,
target_fpfh, voxel_size):
distance_threshold = voxel_size * 1.5
print(":: RANSAC registration on downsampled point clouds.")
print(" Since the downsampling voxel size is %.3f," % voxel_size)
print(" we use a liberal distance threshold %.3f." % distance_threshold)
result = op3.registration_ransac_based_on_feature_matching(
source_down, target_down, source_fpfh, target_fpfh, distance_threshold,
op3.TransformationEstimationPointToPoint(False), 4, [
op3.CorrespondenceCheckerBasedOnEdgeLength(0.9),
op3.CorrespondenceCheckerBasedOnDistance(distance_threshold)
],
op3.RANSACConvergenceCriteria(4000000, 500)
# op3.RANSACConvergenceCriteria(10000000, 20000)
)
return result
def register(self):
start = time.time()
result = open3d.registration_ransac_based_on_feature_matching(
self.source_open3d, self.target_open3d, self.source_fpfh,
self.target_fpfh, self.epsilon,
open3d.TransformationEstimationPointToPoint(False), 4, [
open3d.CorrespondenceCheckerBasedOnEdgeLength(0.95),
open3d.CorrespondenceCheckerBasedOnDistance(self.epsilon)
],
open3d.RANSACConvergenceCriteria(self.max_iteration,
self.max_validation))
end = time.time()
self.time = end - start
self.transformation = result.transformation
return
def register2Fragments(id1, id2, keyptspath, descpath, resultpath, logpath, gtLog, desc_name, inlier_ratio, distance_threshold):
"""
Register point cloud {id1} and {id2} using the keypts location and descriptors.
"""
cloud_bin_s = f'cloud_bin_{id1}'
cloud_bin_t = f'cloud_bin_{id2}'
write_file = f'{cloud_bin_s}_{cloud_bin_t}.rt.txt'
if os.path.exists(os.path.join(resultpath, write_file)):
return 0, 0, 0
source_keypts = get_keypts(keyptspath, cloud_bin_s)
target_keypts = get_keypts(keyptspath, cloud_bin_t)
source_desc = get_desc(descpath, cloud_bin_s, desc_name)
target_desc = get_desc(descpath, cloud_bin_t, desc_name)
source_desc = np.nan_to_num(source_desc)
target_desc = np.nan_to_num(target_desc)
# Select {num_keypts} points based on the scores. The descriptors and keypts are already sorted based on the detection score.
num_keypts = 250
source_keypts = source_keypts[-num_keypts:, :]
source_desc = source_desc[-num_keypts:, :]
target_keypts = target_keypts[-num_keypts:, :]
target_desc = target_desc[-num_keypts:, :]
# Select {num_keypts} points randomly.
# num_keypts = 250
# source_indices = np.random.choice(range(source_keypts.shape[0]), num_keypts)
# target_indices = np.random.choice(range(target_keypts.shape[0]), num_keypts)
# source_keypts = source_keypts[source_indices, :]
# source_desc = source_desc[source_indices, :]
# target_keypts = target_keypts[target_indices, :]
# target_desc = target_desc[target_indices, :]
key = f'{cloud_bin_s.split("_")[-1]}_{cloud_bin_t.split("_")[-1]}'
if key not in gtLog.keys():
# skip the pairs that have less than 30% overlap.
num_inliers = 0
inlier_ratio = 0
gt_flag = 0
else:
# build correspondence set in feature space.
corr = build_correspondence(source_desc, target_desc)
# calculate the inlier ratio, this is for Feature Matching Recall.
gt_trans = gtLog[key]
frag1 = source_keypts[corr[:, 0]]
frag2_pc = open3d.PointCloud()
frag2_pc.points = open3d.utility.Vector3dVector(target_keypts[corr[:, 1]])
frag2_pc.transform(gt_trans)
frag2 = np.asarray(frag2_pc.points)
distance = np.sqrt(np.sum(np.power(frag1 - frag2, 2), axis=1))
num_inliers = np.sum(distance < distance_threshold)
if num_inliers / len(distance) < inlier_ratio:
print(key)
print("num_corr:", len(corr), "inlier_ratio:", num_inliers / len(distance))
inlier_ratio = num_inliers / len(distance)
gt_flag = 1
# calculate the transformation matrix using RANSAC, this is for Registration Recall.
source_pcd = open3d.PointCloud()
source_pcd.points = open3d.utility.Vector3dVector(source_keypts)
target_pcd = open3d.PointCloud()
target_pcd.points = open3d.utility.Vector3dVector(target_keypts)
s_desc = open3d.registration.Feature()
s_desc.data = source_desc.T
t_desc = open3d.registration.Feature()
t_desc.data = target_desc.T
result = open3d.registration_ransac_based_on_feature_matching(
source_pcd, target_pcd, s_desc, t_desc,
0.05,
open3d.TransformationEstimationPointToPoint(False), 3,
[open3d.CorrespondenceCheckerBasedOnEdgeLength(0.9),
open3d.CorrespondenceCheckerBasedOnDistance(0.05)],
open3d.RANSACConvergenceCriteria(50000, 1000))
# write the transformation matrix into .log file for evaluation.
with open(os.path.join(logpath, f'{desc_name}_{timestr}.log'), 'a+') as f:
trans = result.transformation
trans = np.linalg.inv(trans)
s1 = f'{id1}\t {id2}\t 37\n'
f.write(s1)
f.write(f"{trans[0,0]}\t {trans[0,1]}\t {trans[0,2]}\t {trans[0,3]}\t \n")
f.write(f"{trans[1,0]}\t {trans[1,1]}\t {trans[1,2]}\t {trans[1,3]}\t \n")
f.write(f"{trans[2,0]}\t {trans[2,1]}\t {trans[2,2]}\t {trans[2,3]}\t \n")
f.write(f"{trans[3,0]}\t {trans[3,1]}\t {trans[3,2]}\t {trans[3,3]}\t \n")
# write the result into resultpath so that it can be re-shown.
s = f"{cloud_bin_s}\t{cloud_bin_t}\t{num_inliers}\t{inlier_ratio:.8f}\t{gt_flag}"
with open(os.path.join(resultpath, f'{cloud_bin_s}_{cloud_bin_t}.rt.txt'), 'w+') as f:
f.write(s)
return num_inliers, inlier_ratio, gt_flag
def main(config):
test_loader = make_data_loader(
config, config.test_phase, 1, num_threads=config.test_num_thread, shuffle=True)
num_feats = 1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
Model = load_model(config.model)
model = Model(num_feats, config.model_n_out, config=config)
checkpoint = torch.load(config.save_dir + '/checkpoint.pth')
model.load_state_dict(checkpoint['state_dict'])
model = model.to(device)
model.eval()
success_meter, loss_meter, rte_meter, rre_meter = AverageMeter(), AverageMeter(
), AverageMeter(), AverageMeter()
data_timer, feat_timer, reg_timer = Timer(), Timer(), Timer()
test_iter = test_loader.__iter__()
N = len(test_iter)
n_gpu_failures = 0
# downsample_voxel_size = 2 * config.voxel_size
for i in range(len(test_iter)):
data_timer.tic()
try:
xyz0, xyz1, coord0, coord1, feats0, feats1, matching01, T_gth, len_01 = test_iter.next(
)
except ValueError:
n_gpu_failures += 1
logging.info(f"# Erroneous GPU Pair {n_gpu_failures}")
continue
data_timer.toc()
xyz0np, xyz1np = xyz0.numpy(), xyz1.numpy()
pcd0 = make_open3d_point_cloud(xyz0np)
pcd1 = make_open3d_point_cloud(xyz1np)
with torch.no_grad():
feat_timer.tic()
sinput0 = ME.SparseTensor(feats0, coords=coord0).to(device)
F0 = model(sinput0).F.detach()
sinput1 = ME.SparseTensor(feats1, coords=coord1).to(device)
F1 = model(sinput1).F.detach()
feat_timer.toc()
feat0 = make_open3d_feature(F0, 32, F0.shape[0])
feat1 = make_open3d_feature(F1, 32, F1.shape[0])
reg_timer.tic()
distance_threshold = config.voxel_size * 1.0
ransac_result = o3d.registration_ransac_based_on_feature_matching(
pcd0, pcd1, feat0, feat1, distance_threshold,
o3d.TransformationEstimationPointToPoint(False), 4, [
o3d.CorrespondenceCheckerBasedOnEdgeLength(0.9),
o3d.CorrespondenceCheckerBasedOnDistance(distance_threshold)
], o3d.RANSACConvergenceCriteria(4000000, 10000))
T_ransac = torch.from_numpy(ransac_result.transformation.astype(np.float32))
reg_timer.toc()
loss_ransac = corr_dist(T_ransac, T_gth, xyz0, xyz1, weight=None, max_dist=1)
# Translation error
rte = np.linalg.norm(T_ransac[:3, 3] - T_gth[:3, 3])
rre = np.arccos((np.trace(T_ransac[:3, :3].t() @ T_gth[:3, :3]) - 1) / 2)
# Check if the ransac was successful. successful if rte < 2m and rre < 5◦
# http://openaccess.thecvf.com/content_ECCV_2018/papers/Zi_Jian_Yew_3DFeat-Net_Weakly_Supervised_ECCV_2018_paper.pdf
if rte < 2:
rte_meter.update(rte)
if not np.isnan(rre) and rre < np.pi / 180 * 5:
rre_meter.update(rre)
if rte < 2 and not np.isnan(rre) and rre < np.pi / 180 * 5:
success_meter.update(1)
else:
success_meter.update(0)
logging.info(f"Failed with RTE: {rte}, RRE: {rre}")
loss_meter.update(loss_ransac)
if i % 10 == 0:
logging.info(
f"{i} / {N}: Data time: {data_timer.avg}, Feat time: {feat_timer.avg}," +
f" Reg time: {reg_timer.avg}, Loss: {loss_meter.avg}, RTE: {rte_meter.avg}," +
f" RRE: {rre_meter.avg}, Success: {success_meter.sum} / {success_meter.count}" +
f" ({success_meter.avg * 100} %)"
)
data_timer.reset()
feat_timer.reset()
reg_timer.reset()
logging.info(
f"Total loss: {loss_meter.avg}, RTE: {rte_meter.avg}, var: {rte_meter.var}," +
f" RRE: {rre_meter.avg}, var: {rre_meter.var}, Success: {success_meter.sum} " +
f"/ {success_meter.count} ({success_meter.avg * 100} %)"
)