RGBD integration

Open3D implements a scalable RGBD image integration algorithm. The algorithm is based on the technique presented in [Curless1996] and [Newcombe2011]. In order to support large scenes, we use a hierarchical hashing structure introduced in Integrater in ElasticReconstruction.

Read trajectory from .log file

This tutorial uses the function read_trajectory to read a camera trajectory from a .log file. A sample .log file is as follows.

# odometry.log
0   0   1
1   0   0   2
0   1   0   2
0   0   1 -0.3
0   0   0   1
1   1   2
0.999988  3.08668e-005  0.0049181  1.99962
-8.84184e-005  0.999932  0.0117022  1.97704
-0.0049174  -0.0117024  0.999919  -0.300486
0  0  0  1

class CameraPose:

    def __init__(self, meta, mat):
        self.metadata = meta
        self.pose = mat

    def __str__(self):
        return 'Metadata : ' + ' '.join(map(str, self.metadata)) + '\n' + \
            "Pose : " + "\n" + np.array_str(self.pose)


def read_trajectory(filename):
    traj = []
    with open(filename, 'r') as f:
        metastr = f.readline()
        while metastr:
            metadata = list(map(int, metastr.split()))
            mat = np.zeros(shape=(4, 4))
            for i in range(4):
                matstr = f.readline()
                mat[i, :] = np.fromstring(matstr, dtype=float, sep=' \t')
            traj.append(CameraPose(metadata, mat))
            metastr = f.readline()
    return traj

redwood_rgbd = o3d.data.SampleRedwoodRGBDImages()
camera_poses = read_trajectory(redwood_rgbd.odometry_log_path)

TSDF volume integration

Open3D provides two types of TSDF volumes: UniformTSDFVolume and ScalableTSDFVolume. The latter is recommended since it uses a hierarchical structure and thus supports larger scenes.

ScalableTSDFVolume has several parameters. voxel_length = 4.0 / 512.0 means a single voxel size for TSDF volume is \(\frac{4.0\mathrm{m}}{512.0} = 7.8125\mathrm{mm}\). Lowering this value makes a high-resolution TSDF volume, but the integration result can be susceptible to depth noise. sdf_trunc = 0.04 specifies the truncation value for the signed distance function (SDF). When color_type = TSDFVolumeColorType.RGB8, 8 bit RGB color is also integrated as part of the TSDF volume. Float type intensity can be integrated with color_type = TSDFVolumeColorType.Gray32 and convert_rgb_to_intensity = True. The color integration is inspired by PCL.

volume = o3d.pipelines.integration.ScalableTSDFVolume(
    voxel_length=4.0 / 512.0,
    sdf_trunc=0.04,
    color_type=o3d.pipelines.integration.TSDFVolumeColorType.RGB8)

for i in range(len(camera_poses)):
    print("Integrate {:d}-th image into the volume.".format(i))
    color = o3d.io.read_image(redwood_rgbd.color_paths[i])
    depth = o3d.io.read_image(redwood_rgbd.depth_paths[i])
    rgbd = o3d.geometry.RGBDImage.create_from_color_and_depth(
        color, depth, depth_trunc=4.0, convert_rgb_to_intensity=False)
    volume.integrate(
        rgbd,
        o3d.camera.PinholeCameraIntrinsic(
            o3d.camera.PinholeCameraIntrinsicParameters.PrimeSenseDefault),
        np.linalg.inv(camera_poses[i].pose))

Integrate 0-th image into the volume.
Integrate 1-th image into the volume.
Integrate 2-th image into the volume.
Integrate 3-th image into the volume.
Integrate 4-th image into the volume.

Extract a mesh

Mesh extraction uses the marching cubes algorithm [LorensenAndCline1987].

print("Extract a triangle mesh from the volume and visualize it.")
mesh = volume.extract_triangle_mesh()
mesh.compute_vertex_normals()
o3d.visualization.draw_geometries([mesh],
                                  front=[0.5297, -0.1873, -0.8272],
                                  lookat=[2.0712, 2.0312, 1.7251],
                                  up=[-0.0558, -0.9809, 0.1864],
                                  zoom=0.47)

Extract a triangle mesh from the volume and visualize it.

Note:

TSDF volume works like a weighted average filter in 3D space. If more frames are integrated, the volume produces a smoother and nicer mesh. Please check Make fragments for more examples.