Recent voxel-based 3D object detectors for autonomous vehicles learn point cloud
representations either from bird eye view (BEV) or range view (RV, aka the perspective …

Abstract We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for
point cloud based 3D object detection for autonomous driving. Recent studies show that 2D …

Abstract We present Voxel Transformer (VoTr), a novel and effective voxel-based
Transformer backbone for 3D object detection from point clouds. Conventional 3D …

In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object
detector with a voxel-to-point decoder. To preserve the necessary information from all raw …

Many recent works on 3D object detection have focused on designing neural network
architectures that can consume point cloud data. While these approaches demonstrate …

Recent advances on 3D object detection heavily rely on how the 3D data are represented,
ie, voxel-based or point-based representation. Many existing high performance 3D detectors …

In this work, we present a unified framework for multi-modality 3D object detection, named
UVTR. The proposed method aims to unify multi-modality representations in the voxel space …

Existing point-cloud based 3D object detectors use convolution-like operators to process
information in a local neighbourhood with fixed-weight kernels and aggregate global context …

Recent work on 3D object detection advocates point cloud voxelization in birds-eye view,
where objects preserve their physical dimensions and are naturally separable. When …

Abstract 3D object detection based on LiDAR point cloud has wide applications in
autonomous driving and robotics. Recently, many approaches use voxelization …