/pixor_ovo

用pixor处理有色点云

Primary LanguagePython

Introduction

This is an unofficial inplementation of [Bird's Eye View Object Detection Algorithm for self-driving Cars] PIXOR in Pytorch. A large part of this project is based on the work here. Thanks to @Ankita Kalra. This work is still ongoing. #requirement You should have the pointcloud with shape (n, 7) in the bin file. 点云应该是有颜色(x,y,z,r,g,b,i)的点云.你也可以处理(x,y,z,i)格式的点云.需要稍作调整. 这个比我前一个pixor版本的要好些.更稳定一些.

Dependencies

  • python3.5+
  • Pytorch (tested on 0.4.1)
  • opencv-python
  • shapely
  • matplotlib
  • tensorboardX

Installation

  1. Clone this repository.

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. Split the training set into training and validation set according to the protocol here. And rearrange the folders to have the following structure:

└── KITTI
       ├── training   <-- training data
       |   ├── image_2
       |   ├── label_2
       |   └── velodyne
       └── validation  <--- evaluation data
       |   ├── image_2
       |   ├── label_2
       |   └── velodyne
       |
       |__ train.txt
       |
       |__ val.txt
       |
       |__ trainval.txt

Train

$ python run_training.py
  1. There is a pre-trained model for car in pretrained_models/model_90.pth.

inference

$ python run_inference.py

results

prediction for rgb pointcloud(add se_module) input shape (800, 700. 36+3) output shape (800, 700, 7)

example1 example2

TODO

  • improve the performances
  • provide SummaryWriter()
  • provide run_evaluate.py