Scanet: Spatial-channel Attention Network for 3D Object Detection

This paper aims to achieve high-accuracy 3D object detection, in which we propose a novel Spatial-Channel Attention Network (SCANet), a two-stage detector that takes both LIDAR point clouds and RGB images as input to generate 3D object estimates. The first stage is a 3D region proposal network (RPN) in which we put forward a new Spatial-Channel Attention (SCA) module and an Extension Spatial Upsample (ESU) module. Using the pyramid pooling structure and global average pooling, the SCA module can not only effectively incorporate multi-scale and global context information, but also produce spatial and channel-wise attention to select discriminative features. The ESU module in the decoder can recover the lost spatial information caused by consecutive pooling operators to generate reliable 3D region proposals. In the second stage, we design a new multi-level fusion scheme for accurate classification and 3D bounding box regression. Experimental results demonstrate that SCANet achieves state-of-the-art performance on the challenging KITTI 3D object detection benchmark.