down_factor = args.downsample_factor
intrinsic /= down_factor
intrinsic[2, 2] = 1
# Only use pose 0 for 1-th frame for alignment.
# DO NOT use other gt poses here
T_cam_to_world = gt_poses[0]
T_gt = []
T_est = []
for i in range(args.start_idx, args.end_idx + 1):
print('loading frame {}'.format(i))
depth = o3d.io.read_image('{}/{}.png'.format(depth_path, i))
depth = np.asarray(depth) / depth_scale
depth = depth[::down_factor, ::down_factor]
vertex_map = transforms.unproject(depth, intrinsic)
color_map = np.asarray(o3d.io.read_image('{}/{}.png'.format(rgb_path, i))).astype(float) / 255.0
color_map = color_map[::down_factor, ::down_factor]
normal_map = np.load('{}/{}.npy'.format(normal_path, i))
normal_map = normal_map[::down_factor, ::down_factor]
if i > 1:
print('Frame-to-model icp')
T_world_to_cam = np.linalg.inv(T_cam_to_world)
T_world_to_cam = icp(m.points[::down_factor],
m.normals[::down_factor],
vertex_map,
normal_map,
intrinsic,
intrinsic_struct = o3d.io.read_pinhole_camera_intrinsic('intrinsics.json')
intrinsic = np.array(intrinsic_struct.intrinsic_matrix)
depth_path = os.path.join(args.path, 'depth')
normal_path = os.path.join(args.path, 'normal')
# TUM convention -- uint16 value to float meters
depth_scale = 5000.0
# Source: load depth and rescale to meters
source_depth = o3d.io.read_image('{}/{}.png'.format(
depth_path, args.source_idx))
source_depth = np.asarray(source_depth) / depth_scale
# Unproject depth to vertex map (H, W, 3) and reshape to a point cloud (H*W, 3)
source_vertex_map = transforms.unproject(source_depth, intrinsic)
source_points = source_vertex_map.reshape((-1, 3))
# Load normal map (H, W, 3) and reshape to point cloud normals (H*W, 3)
source_normal_map = np.load('{}/{}.npy'.format(normal_path,
args.source_idx))
source_normals = source_normal_map.reshape((-1, 3))
# Similar preparation for target, but keep the image format for projective association
target_depth = o3d.io.read_image('{}/{}.png'.format(
depth_path, args.target_idx))
target_depth = np.asarray(target_depth) / depth_scale
target_vertex_map = transforms.unproject(target_depth, intrinsic)
target_normal_map = np.load('{}/{}.npy'.format(normal_path,
args.target_idx))
