The KITTI Vision Benchmark Suite

We propose a self-supervised (SSL) generic point cloud pre-training















method to improve the 3D object detection task quality. This is the















first study that shows learned motion and flow representations in a















spatio-temporal context via self-supervised training, which can be















utilized for downstream spatial 3D vision tasks. We submit our















PointGNN results trained with our SSL pipeline.

1400k step training, T=3, PointGNN car network

@article{erccelik20223d,

title={3D Object Detection with a Self-supervised Lidar Scene Flow
Backbone},

author={Erçelik, Emeç and Yurtsever, Ekim and Liu, Mingyu and
Yang, Zhijie and Zhang, Hanzhen and Topçam, Pınar and Listl,
Maximilian and Çaylı, Yılmaz Kaan and Knoll, Alois},

journal={arXiv preprint arXiv:2205.00705},

year={2022}

}



journal={arXiv preprint arXiv:2205.00705},



year={2022}



}