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demo_vigohome.py
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import numpy as np
import torch
import open3d as o3d
from network import PointNetFeature
import copy
from lrf import lrf
model_root = './model'
patch_size = 256
dim = 32
perc = 5
batch_size = 500
pts_to_sample = 5000
# dataset specific parameters
voxel_size = .01
lrf_kernel = .6 * np.sqrt(3)
net = PointNetFeature(dim=dim, l2norm=True, tnet=True)
ckpt_name = 'final_chkpt.pth'
checkpoint = '{}/{}'.format(model_root, ckpt_name)
net.load_state_dict(torch.load(checkpoint))
net.cuda()
net.eval()
print('*****************************')
print('demo VigoHome dataset')
print('*****************************')
print('point clouds in their original reference frame ...')
print('... press \'q\' to continue ...')
pcd1 = o3d.io.read_point_cloud('assets/vigohome_bathroom_upstairs.ply')
pcd2 = o3d.io.read_point_cloud('assets/vigohome_livingroom_downstairs.ply')
pcd1.paint_uniform_color([128 / 255, 128 / 255, 128 / 255])
pcd2.paint_uniform_color([0 / 255, 76 / 255, 153 / 255])
pcd1 = pcd1.voxel_down_sample(voxel_size)
pcd2 = pcd2.voxel_down_sample(voxel_size)
pcd1.estimate_normals()
pcd2.estimate_normals()
o3d.visualization.draw_geometries([pcd1, pcd2])
# random sampling of points
inds1 = np.random.choice(np.asarray(pcd1.points).shape[0], pts_to_sample, replace=False)
print()
print('randomly sampled {} out of {} from pcd1 to compute DIPs'.format(pts_to_sample, np.asarray(pcd1.points).shape[0]))
inds2 = np.random.choice(np.asarray(pcd2.points).shape[0], pts_to_sample, replace=False)
print('randomly sampled {} out of {} from pcd2 to compute DIPs'.format(pts_to_sample, np.asarray(pcd2.points).shape[0]))
pcd1_pts = np.asarray(pcd1.points)[inds1]
pcd2_pts = np.asarray(pcd2.points)[inds2]
# compute DIPs
frag1_lrf = lrf(pcd=pcd1,
pcd_tree=o3d.geometry.KDTreeFlann(pcd1),
patch_size=patch_size,
lrf_kernel=lrf_kernel,
viz=False) # set viz=True to visualise patch kernels and their respective LRF
frag2_lrf = lrf(pcd=pcd2,
pcd_tree=o3d.geometry.KDTreeFlann(pcd2),
patch_size=patch_size,
lrf_kernel=lrf_kernel,
viz=False) # set viz=True to visualise patch kernels and their respective LRF
patches1 = np.empty((pcd1_pts.shape[0], 3, patch_size))
patches2 = np.empty((pcd2_pts.shape[0], 3, patch_size))
print()
print('computing patches and LRFs ...')
for i in range(pcd1_pts.shape[0]):
frag1_lrf_pts, _, _ = frag1_lrf.get(pcd1_pts[i])
frag2_lrf_pts, _, _ = frag2_lrf.get(pcd2_pts[i])
patches1[i] = frag1_lrf_pts.T
patches2[i] = frag2_lrf_pts.T
print('... done')
pcd1_desc = np.empty((patches1.shape[0], dim))
pcd2_desc = np.empty((patches2.shape[0], dim))
pcd1_mx = np.empty((patches1.shape[0], 1024))
pcd2_mx = np.empty((patches2.shape[0], 1024))
pcd1_amx = np.empty((patches1.shape[0], 1024), dtype=int)
pcd2_amx = np.empty((patches2.shape[0], 1024), dtype=int)
print()
print('processing patches with PointNet ...')
for b in range(int(np.ceil(patches1.shape[0] / batch_size))):
i_start = b * batch_size
i_end = (b + 1) * batch_size
if i_end > pts_to_sample:
i_end = pts_to_sample
pcd1_batch = torch.Tensor(patches1[i_start:i_end]).cuda()
with torch.no_grad():
f, mx1, amx1 = net(pcd1_batch)
pcd1_desc[i_start:i_end] = f.cpu().detach().numpy()[:i_end-i_start]
pcd1_mx[i_start:i_end] = mx1.cpu().detach().numpy().squeeze()[:i_end - i_start]
pcd1_amx[i_start:i_end] = amx1.cpu().detach().numpy().squeeze()[:i_end - i_start]
pcd2_batch = torch.Tensor(patches2[i_start:i_end]).cuda()
with torch.no_grad():
f, mx2, amx2 = net(pcd2_batch)
pcd2_desc[i_start:i_end] = f.cpu().detach().numpy()[:i_end-i_start]
pcd2_mx[i_start:i_end] = mx2.cpu().detach().numpy().squeeze()[:i_end - i_start]
pcd2_amx[i_start:i_end] = amx2.cpu().detach().numpy().squeeze()[:i_end - i_start]
mag_pcd1_mx = np.linalg.norm(pcd1_mx, axis=1)
mag_pcd2_mx = np.linalg.norm(pcd2_mx, axis=1)
good_pcd1_desc = mag_pcd1_mx > np.percentile(mag_pcd1_mx, perc)
good_pcd2_desc = mag_pcd2_mx > np.percentile(mag_pcd2_mx, perc)
pcd1_desc = pcd1_desc[good_pcd1_desc]
pcd2_desc = pcd2_desc[good_pcd2_desc]
print('... done')
# ransac
pcd1_dsdv = o3d.registration.Feature()
pcd2_dsdv = o3d.registration.Feature()
pcd1_dsdv.data = pcd1_desc.T
pcd2_dsdv.data = pcd2_desc.T
pcd1_pts = pcd1_pts[good_pcd1_desc]
pcd2_pts = pcd2_pts[good_pcd2_desc]
_pcd1 = o3d.geometry.PointCloud()
_pcd1.points = o3d.utility.Vector3dVector(pcd1_pts)
_pcd2 = o3d.geometry.PointCloud()
_pcd2.points = o3d.utility.Vector3dVector(pcd2_pts)
print()
print('estimating transformation with RANSAC ...')
est_result12 = o3d.registration.registration_ransac_based_on_feature_matching(
_pcd1,
_pcd2,
pcd1_dsdv,
pcd2_dsdv,
.05,
estimation_method=o3d.registration.TransformationEstimationPointToPoint(False),
ransac_n=3,
checkers=[o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(.9),
o3d.registration.CorrespondenceCheckerBasedOnDistance(.05)],
criteria=o3d.registration.RANSACConvergenceCriteria(50000, 500)
)
print('... done')
pcd1a = copy.deepcopy(pcd1)
pcd2a = copy.deepcopy(pcd2)
print()
print('estimated transformation to align pcd1 to pcd2:')
print(est_result12.transformation)
pcd1a.transform(est_result12.transformation)
print()
print('point clouds in a common reference frame after estimated transformation ...')
print('... press \'q\' to continue ...')
o3d.visualization.draw_geometries([pcd1a, pcd2a])