Unable to visualize the PCD segemntation with open3d, pcl, cloudcompare

[yekruz]

Hi,

I have tried several ways to viusalize the semantic segmentation annotation outputs in pcd format, but the annotations are not being shown. Any help is appreciated.

I am using the following code. I see the original pcd ,but the annotation are not shown. Only reading the PCD alone also didn't work.

import open3d as o3d
import numpy as np

# Read the original point cloud with coordinates
pcd = o3d.io.read_point_cloud("20220929_083807_525890.pcd")

# Read segmentation data
with open('20220929_083807_549617_lidar_point_cloud_0_segmentation.pcd', 'rb') as f:
    # Skip header
    for _ in range(11):
        f.readline()

    seg_data = np.frombuffer(f.read(), dtype=np.uint8)

# From the histogram, we can see major semantic classes
semantic_colors = {
    1: [1, 0, 0],     # Red (largest peak in histogram)
    128: [0, 1, 0],   # Green (second major peak)
    156: [0, 0, 1],   # Blue
    40: [1, 1, 0],    # Yellow
    122: [1, 0, 1],   # Magenta
    8: [0, 1, 1],     # Cyan
}

# Create colors array
colors = np.zeros((len(seg_data), 3))

print(seg_data)
# Assign colors based on semantic classes
for class_id, color in semantic_colors.items():
    mask = (seg_data == class_id)
    colors[mask] = color

# For unassigned classes, use gray
unassigned_mask = ~np.any([seg_data == key for key in semantic_colors.keys()], axis=0)
colors[unassigned_mask] = [0.5, 0.5, 0.5]  # Gray for unassigned points

# Assign colors to point cloud
pcd.colors = o3d.utility.Vector3dVector(colors)

# Visualization
vis = o3d.visualization.Visualizer()
vis.create_window()
vis.add_geometry(pcd)

# Improve visualization settings
opt = vis.get_render_option()
opt.background_color = np.asarray([0, 0, 0])  # Black background
opt.point_size = 2.0

# Add coordinate frame
coordinate_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(
    size=1.0, origin=[0, 0, 0])
vis.add_geometry(coordinate_frame)

vis.run()
vis.destroy_window()

for reading the annotation PCD file alone:

import open3d as o3d
import numpy as np

# Read the PCD file
pcd = o3d.io.read_point_cloud("20220929_083807_549617_lidar_point_cloud_0_segmentation.pcd")

# Assuming semantic labels are stored in the 'labels' attribute
labels = np.asarray(pcd.labels)

# Create a color map for different semantic classes
color_map = {
    0: [1, 0, 0],  # Red for class 0
    1: [0, 1, 0],  # Green for class 1
    2: [0, 0, 1],  # Blue for class 2
    # Add more colors for additional classes
}

# Assign colors based on semantic labels
colors = np.array([color_map[label] for label in labels])
pcd.colors = o3d.utility.Vector3dVector(colors)

# Visualize the semantically colored point cloud
o3d.visualization.draw_geometries([pcd])

using the output with x,y,z,seg also does not work and the following code, colorizes the objects instance-wise and not semantic-wise

import open3d as o3d
import numpy as np
import struct

def read_binary_pcd(filename):
    with open(filename, 'rb') as f:
        # Skip header
        for _ in range(11):
            f.readline()
        binary_data = f.read()
        
        fmt = 'fffi'
        size = struct.calcsize(fmt)
        num_points = len(binary_data) // size
        points = []
        for i in range(num_points):
            offset = i * size
            pt = struct.unpack(fmt, binary_data[offset:offset + size])
            points.append(pt)
        
        points = np.array(points)
        return points[:, :3], points[:, 3].astype(np.int32)

# Define semantic class colors based on the plateaus in the visualization
semantic_colors = {
    0: [0.7, 0.7, 0.7],    # background
    10: [1.0, 0.0, 0.0],   # road
    40: [0.0, 1.0, 0.0],   # vegetation
    80: [0.0, 0.0, 1.0],   # building
    120: [1.0, 1.0, 0.0],  # car
    130: [1.0, 0.0, 1.0],  # pedestrian
    140: [0.0, 1.0, 1.0],  # pole
    150: [0.8, 0.4, 0.0],  # traffic sign
    160: [0.5, 0.5, 0.5]   # other
}

# Read point cloud
xyz, seg_labels = read_binary_pcd("20220929_084039_549664_lidar_point_cloud_0_segmentation.pcd")

# Create Open3D point cloud
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyz)

# Create colors array
colors = np.zeros((len(seg_labels), 3))

# Assign colors based on semantic classes
# Round the segmentation labels to the nearest semantic class
for label in np.unique(seg_labels):
    # Find the closest semantic class
    closest_class = min(semantic_colors.keys(), key=lambda x: abs(x - label))
    mask = (seg_labels == label)
    colors[mask] = semantic_colors[closest_class]

# Assign colors to point cloud
pcd.colors = o3d.utility.Vector3dVector(colors)

# Visualization
vis = o3d.visualization.Visualizer()
vis.create_window()
vis.add_geometry(pcd)

# Improve visualization settings
opt = vis.get_render_option()
opt.background_color = np.asarray([0, 0, 0])
opt.point_size = 2.0

vis.run()
vis.destroy_window()