MVGNet: 3D object detection using Multi-Volume Grid representation in urban traffic scenarios

Recent advances in 3D object detection focus on combining multiple 3D data representations in order to leverage each 3D representation. However, this opens a new issue: increased need for hardware memory and computational resources which are unfortunately limited.In this paper we explore a new 3D data representation, the Multi-Volume Grid representation for detecting 3D objects in traffic scenes. The MVG structure efficiently maps free and occupied space from complex environments, where hanging and protruding objects are present. In order to integrate the MVG structure, we design MVGNet, a multi-branch network. We capture the essence of the MVG’s dynamic sized pillar structures by proposing two new input point descriptors and by using simple yet efficient spatial and channel based attention mechanisms throughout our network. We evaluate our method on KITTI dataset on three classes: pedestrian, cyclist, car. Our method achieves state of the art results with a significant improvement for the pedestrian and cyclist classes.