Train in Germany, Test in The USA: Making 3D Object Detectors Generalize

3D_adapt_auto_driving

This paper has been accpeted by Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

by Yan Wang*, Xiangyu Chen*, Yurong You, Li Erran, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao*

Dependencies

Usage

Prepare Datasets (Jupyter notebook)

We develop our method on these datasets:

Configure dataset_path in config_path.py.

Raw datasets will be organized as the following structure:

 dataset_path/

     | kitti/               # KITTI object detection 3D dataset

         | training/

         | testing/

     | argo/                # Argoverse dataset v1.1

         | train1/

         | train2/

         | train3/

         | train4/

         | val/

         | test/

     | nusc/                # nuScenes dataset v1.0

         | maps/

         | samples/

         | sweeps/

         | v1.0-trainval/
 
 

 
To finish reading, please visit source site