Virtual Sparse Convolution for Multimodal 3D Object Detection

Recently, virtuall pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inaccurate depth completion significantly degrade detection precision. This paper proposes a fast yet effective backbone, termed Vir-ConvNet, based on a new operator VirConv (Virtual Sparse Convolution), for virtual-point-based 3D object detection. VirConv consists of two key designs: (1) StVD (Stochastic Voxel Discard) and (2) NRConv (Noise-Resistant Sub-manifold Convolution). StVD alleviates the…

• NN
  National Natural Science Foundation of China

  Award: 62171393
• FR
  Fundamental Research Funds for the Central Universities

  Award: 20720220064