Improved point-voxel region convolutional neural network for small object detection

With the widespread use of LiDAR sensors, 3D object detection through 3D point cloud data processing has become a research target in robotics and autonomous driving. However, the disorder and sparsity of point cloud data are the problems in traditional point cloud data processing. It is challenging to detect objects using a large amount of point cloud data. Conventional 3D object detectors have mainly grid-based methods and point-based methods. PV-RCNN proposed a framework that combines voxel-based and point-based techniques, and object features are extracted using 3D voxel CNNs. However, the resolution reduction caused by the CNN affects the localization of objects. This study aims to improve the detection accuracy of more minor things by feeding not only a single output of the voxel CNN but also multiple outputs, including high-resolution outputs, to the RPN. We came out with a new network that introduces the Multi-Scale Region Proposal Network to reduce the effect of resolution degradation. Our network has better recognition accuracy for small objects like bicycles than the original PV-RCNN. In extensive experiments, we demonstrate that our model has a 5% improvement for small things, such as cyclists training on the KITTI dataset.