The KITTI Vision Benchmark Suite

We proposes a fast yet effective backbone, termed
VirConvNet, 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 Submanifold
Convolution). StVD alleviates the computation
problem by discarding large amounts of nearby
redundant voxels. NRConv tackles the noise problem
by encoding voxel features in both 2D image and 3D
LiDAR space. By integrating VirConv, we first
develop an efficient pipeline VirConv-L based on
an early fusion design. Then, we build a high-
precision pipeline VirConv-T based on a
transformed refinement scheme. Finally, we develop
a semi-supervised pipeline VirConv-S based on a
pseudo-label framework.

TBD

@inproceedings{VirConv,
title={Virtual Sparse Convolution for Multimodal
3D Object Detection},
author={Wu, Hai and Wen,Chenglu and Shi,
Shaoshuai and Wang, Cheng},
booktitle={CVPR},
year={2023}
}