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Introduction

This is an unofficial implementation of VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection in pytorch. A large part of this project is based on the work here

Dependencies

Installation

  1. Clone this repository.
  2. Compile the Cython module for box_overlaps
$ python3 setup.py build_ext --inplace
  1. Compile the nms model
$ python3 nms/build.py

Data Preparation

  1. Download the 3D KITTI detection dataset from here. Data to download include:

    • Velodyne point clouds (29 GB): input data to VoxelNet
    • Training labels of object data set (5 MB): input label to VoxelNet
    • Camera calibration matrices of object data set (16 MB): for visualization of predictions
    • Left color images of object data set (12 GB): for visualization of predictions

  2. In this project, the cropped point cloud data for training and validation. Point clouds outside the image coordinates are removed.

$ python3 data/crop.py
  1. Split the training set into training and validation set according to the protocol here.
└── DATA_DIR
       ├── training   <-- training data
       |   ├── image_2
       |   ├── label_2
       |   ├── velodyne
       |   └── crop
       └── testing  <--- testing data
       |   ├── image_2
       |   ├── label_2
       |   ├── velodyne
       |   └── crop

Train

TODO