CrossFusion net: Deep 3D object detection based on RGB images and point clouds in autonomous driving

作者：

Highlights：

• The proposed CrossFusion Net performs 3D object detection from two sensors.

• The presented attention mechanism generates adaptive weights for two streams of feature maps.

• The CF Net outperforms 1%, 8%, and 3% APs in easy, moderate, and hard cases, respectively.

• The comparable 100 ms inference time is much less than 170-360 ms from others.

摘要

•The proposed CrossFusion Net performs 3D object detection from two sensors.•The presented attention mechanism generates adaptive weights for two streams of feature maps.•The CF Net outperforms 1%, 8%, and 3% APs in easy, moderate, and hard cases, respectively.•The comparable 100 ms inference time is much less than 170-360 ms from others.

论文关键词：Deep learning,3D object detection,Data fusion,Autonomous driving

论文评审过程：Received 29 December 2019, Revised 20 May 2020, Accepted 21 May 2020, Available online 3 June 2020, Version of Record 25 June 2020.

论文官网地址：https://doi.org/10.1016/j.imavis.2020.103955