Convert point cloud to depth map

point_cloud_to_depth_map.py

import numpy as np

def pointcloud_to_depth_map(pointcloud: np.ndarray, theta_res=150, phi_res=32, max_depth=50, phi_min_degrees=60,
                            phi_max_degrees=100) -> np.ndarray:
    """
        All params are set so they match default carla lidar settings
    """
    assert pointcloud.shape[1] == 3, 'Must have (N, 3) shape'
    assert len(pointcloud.shape) == 2, 'Must have (N, 3) shape'

    xs = pointcloud[:, 0]
    ys = pointcloud[:, 1]
    zs = pointcloud[:, 2]

    rs = np.sqrt(np.square(xs) + np.square(ys) + np.square(zs))

    phi_min = np.deg2rad(phi_min_degrees)
    phi_max = np.deg2rad(phi_max_degrees)
    phi_range = phi_max - phi_min
    phis = np.arccos(zs / rs)

    THETA_MIN = -np.pi
    THETA_MAX = np.pi
    THETA_RANGE = THETA_MAX - THETA_MIN
    thetas = np.arctan2(xs, ys)

    phi_indices = ((phis - phi_min) / phi_range) * (phi_res - 1)
    phi_indices = np.rint(phi_indices).astype(np.int16)

    theta_indices = ((thetas - THETA_MIN) / THETA_RANGE) * theta_res
    theta_indices = np.rint(theta_indices).astype(np.int16)
    theta_indices[theta_indices == theta_res] = 0

    normalized_r = rs / max_depth

    canvas = np.ones(shape=(theta_res, phi_res), dtype=np.float32)
    # We might need to filter out out-of-bound phi values, if min-max degrees doesnt match lidar settings
    canvas[theta_indices, phi_indices] = normalized_r

    return canvas

visualize__point_cloud.main.py

import numpy as np
import matplotlib.pyplot as plt

from pointcloud_to_depthmap import pointcloud_to_depth_map

pointcloud = np.load("lidar.npy")
depth_map = pointcloud_to_depth_map(pointcloud)

depth_map = depth_map * 256
depth_map = np.flip(depth_map, axis=1) # so floor is down
depth_map = np.swapaxes(depth_map, 0, 1)

plt.imshow(depth_map, cmap='gray_r')
plt.show()

@theotziol : Please have a look at this code. https://github.com/balamuruganky/depthmap_to_pcd/blob/master/src/PCLUtils.cpp