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Semantic Segmentation of LiDAR Point Cloud for Autonomous Vehicles

CS541: Deep Learning - Worcester Polytechnic Institute, Spring 2022

Members: Chinmay Madhukar Todankar, Bhushan Ashok Rane, Aniket Manish Patil

Master of Science in Robotics Engineering

Link to Report

Requirements:

  1. CUDA Toolkit + GPU drivers

  2. Tensorflow

  3. Numpy

  4. Matplotlib

  5. Pillow

Dataset - Semantic KITTI

Download the Velodyne sensor data and the Label data folders, and place in the dataset folder in the form as mentioned on the Semantic KITTI website.

  1. Download Point Cloud Data

  2. Download Label Data

We will require the path of this dataset folder as a argument to the run command.

How to run the code:

Go to the parent folder of this repo, that is, semantic_segmentation and enter the command:

python3 scripts/main.py -d **path_to_dataset_folder**

References

  1. A. Milioto and I. Vizzo and J. Behley and C. Stachniss, RangeNet++: Fast and Accurate LiDAR Semantic Segmentation