About inference

[ranhaogong]

Excellent work!
How can I generate the completed point cloud results.
Looking forward to your suggestions!

[kylelo]

Hi @ranhaogong ,

Thank you for trying out our work :)
Here's the example of converting height map into point cloud.

import numpy as np
import open3d as o3d
import matplotlib.cm as cm

def create_point_cloud(height_map, translate=[0, 0, 0], display_range=[0.0, 1.0]):
    height, width = height_map.shape[:2]

    # Creating a point for each pixel in the depth map
    y_idxs, x_idxs = np.indices((height, width))
    x_coords = x_idxs.astype(np.float64) / width
    y_coords = y_idxs.astype(np.float64) / height
    z_coords = height_map.astype(np.float64)

    points = np.column_stack((x_coords.flatten(), y_coords.flatten(), z_coords.flatten()))

    # boolean indexing to find rows where z value is between 0 and 1
    mask = (points[:, 2] >= display_range[0]) & (points[:, 2] <= display_range[1])

    # apply the mask
    points = points[mask]

    # Translating the point cloud
    points += translate


    # Normalize z-coordinates for colormap scaling
    norm_z_coords = (points[:, 2] - display_range[0]) / (display_range[1] - display_range[0])

    # Apply colormap to z-coordinates
    cmap = cm.get_cmap('viridis')
    colors = cmap(norm_z_coords)[:, :3]  # Exclude alpha value

    point_cloud = o3d.geometry.PointCloud()
    point_cloud.points = o3d.utility.Vector3dVector(points)
    point_cloud.colors = o3d.utility.Vector3dVector(colors)

    return point_cloud