Point cloud -> Range Image -> Point Cloud fails

[Petros626]

Hey guys,

I'm trying to successfully reconvert a interpolated range image back to a 3d point cloud with labels, but I fail. The inspiration came from this paper: https://www.mdpi.com/1424-8220/23/1/322.

The conversion of 3D pc to range image is correct, but the reconversion seems not.

Original scene:

Interpolated scene (approach):

The new scene seems somehow curved and not aligned with the gt-boxes, like in the paper.

Paper results and expected output:

Thanks for any help!

[brmarkus]

What actually is your question?
Would you mind sharing your code to see what the community could help you with?

[Petros626]

@brmarkus Hey thank you for the quick answer.

My question is why I can't get a ground plane aligned pc again (image (c) in paper results). I have a curve over the x-axis (see interpolated scene).

Code (minimal Example):

# load validation data
with open(val_filename, 'rb') as f:
     data_list = pickle.load(f)

points = data_list[5]['points'] # (N, 5)
points_xyz_int = points[:, :4]
beam_label = points[:, -1].astype(int)
augmentor = DataAugmentor(root_path=data_path, augmentor_configs=[], class_names=class_names, logger=common_utils.create_logger())
# KITTI scanning parameters, obtained from Hough transformation (source: https://github.com/tusen-ai/RangeDet/blob/5078ce339c6d27a009aed1ca2790911ce4d10bc7/datasets/create_range_image_in_kitti.py#L210)
height = np.loadtxt("/home/rlab10/OpenPCDet/data/kitti/training/kitti scanning parameters/height.txt")
zenith = np.loadtxt("/home/rlab10/OpenPCDet/data/kitti/training/kitti scanning parameters/zenith.txt")
incl = np.loadtxt("/home/rlab10/OpenPCDet/data/kitti/training/kitti scanning parameters/incl.txt")

# 1. Point Cloud -> Low-resolution 2D Range Image
range_image = augmentor_utils.get_range_image_hdl64e(points_xyz_int, incl=incl, height=height)

# 2. Better Upsampling/Interpolation on Range-Image (source: https://www.mdpi.com/1424-8220/23/1/322)
range_image_upsampled_ = augmentor.pixel_distance_range_weighted_interpolation(range_image, MAX_RANGE=70.0)

points_result = augmentor_utils.range_image_to_cartesian(range_image=range_image_upsampled_, incl=incl[::-1], beam_label=True)
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')

x = points_result[:, 0]
y = points_result[:, 1]
z = points_result[:, 2]

ax.scatter(x, y, z, s=0.1, c=z, cmap='plasma')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('3D Point Cloud (points_result)')
plt.show()

Functions:

# source: https://github.com/tusen-ai/RangeDet/blob/1df87b2d9aa9ef3f77ad634c2656b96867c7eac8/datasets/create_range_image_in_kitti.py#L107
def get_range_image_hdl64e(points, incl, height):
    incl_deg = incl * 180 / 3.1415
    # print(incl - np.roll(incl, 1))
    xy_norm = np.linalg.norm(points[:, :2], ord = 2, axis = 1)
    error_list = []
    for i in range(len(incl)):
        h = height[i]
        theta = incl[i]
        error = np.abs(theta - np.arctan2(h - points[:,2], xy_norm))
        error_list.append(error)
    all_error = np.stack(error_list, axis=-1)
    row_inds = np.argmin(all_error, axis=-1)

    azi = np.arctan2(points[:,1], points[:,0])
    # points per beam
    width = 2048 # points per laser per revolution (data sheet: 2083)
    # old
    col_inds = width - 1.0 + 0.5 - (azi + np.pi) / (2.0 * np.pi) * width # right to left (Azimuth +π -> -π)
    col_inds = np.round(col_inds).astype(np.int32)
    col_inds[col_inds == width] = width - 1
    col_inds[col_inds < 0] = 0
    # new
    #col_inds = ((azi + np.pi) / (2.0 * np.pi) * width).astype(np.int32)  # left to right (Azimuth -π -> +π)
    #col_inds = np.clip(col_inds, 0, width - 1)

    # Ch 0: Range
    # Ch 1: x
    # Ch 2: y
    # Ch 3: z
    # Ch 4: intensity
    empty_range_image = np.full((64, width, 5), -1, dtype = np.float32) 
    point_range = np.linalg.norm(points[:,:3], axis = 1, ord = 2)

    order = np.argsort(-point_range) 
    point_range = point_range[order]
    points = points[order]
    row_inds = row_inds[order]
    col_inds = col_inds[order]

    empty_range_image[row_inds, col_inds, :] = np.concatenate([point_range[:,None], points], axis = 1)

    return empty_range_image

def range_image_to_cartesian(range_image, incl, azimuth_min=-np.pi, azimuth_max=np.pi, beam_label=False):
    H, W, C = range_image.shape # (64, 2048, 5)
 
    azimuths = np.linspace(azimuth_min, azimuth_max, W, endpoint=False) 
    inclinations = incl

    azimuth_grid, incl_grid = np.meshgrid(azimuths, inclinations)
    ranges = range_image[:, :, 0]
    intensity = range_image[:, :, 4] if C > 4 else np.zeros_like(ranges)

    mask = ranges > 0
    r = ranges[mask]
    azimuth = azimuth_grid[mask] # θ
    inclination = incl_grid[mask] # φ (inclination, vertical angle from xy-plane)
    intensity = intensity[mask]

    # Spherical to cartesian (where elevation is the polar angle (from the z-axis).)
    x = r * np.cos(inclination) * np.cos(azimuth)
    y = r * np.cos(inclination) * np.sin(azimuth)
    z = r * np.sin(inclination)

    # x = radius * cos(phi) * sin(theta) # phi = ele; theta=azi
    # y = radius * sin(phi) * sin(theta)
    # z = radius * cos(theta)

    if beam_label:
        beam_label = np.repeat(np.arange(H)[:, None], W, axis=1)[mask] # Line number as beam label (see row_inds = beam_labels[order])
        points = np.stack([x, y, z, intensity, beam_label], axis=-1)
    else:
        points = np.stack([x, y, z, intensity], axis=-1)

    return points

with incl=incl in .range_image_to_cartesian(). The scene is upside down, objects are pointing downwards.:

with incl=incl[::1] in .range_image_to_cartesian()`. The scene is curved, but objects are in correct position: