HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

Ye, Maosheng; Xu, Shuangjie; Cao, Tongyi

doi:10.1109/cvpr42600.2020.00170

Cited by 242Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety3D Shape Modeling and Analysis

HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

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Ye, Maosheng, Xu, Shuangjie, Cao, Tongyi·2020·2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr42600.2020.00170

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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 voxelization with per voxel PointNet style feature extractor leads to accurate and efficient detector for large 3D scenes. Since the size of the feature map determines the computation and memory cost, the size of the voxel becomes a parameter that is hard to balance. A smaller voxel size gives a better performance, especially for small objects, but a longer inference time. A larger voxel can cover the same area with a smaller feature map, but fails to capture intricate features and accurate location for smaller objects. We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps. We further propose an attentive voxel feature encoding that outperforms plain VFE and a feature fusion pyramid network to aggregate multi-scale information at feature map level. Experiments on the KITTI benchmark show that a single HVNet achieves the best mAP among all existing methods with a real time inference speed of 31Hz.

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Point-Fusion proposed a dense fusion network that fuses point cloud features extracted by PointNet and image features extracted by ResNet.
“Point-Fusion [78] proposed a dense fusion network that fuses point cloud features extracted by PointNet [105] and image features extracted by ResNet [106].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [78]

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Authors: Ye, M., Xu, S., & Cao, T.
ORCID: Ye, Maosheng
Xu, Shuangjie
Cao, Tongyi
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2020-06
Pages: 1628-1637
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr42600.2020.00170
OpenAlex: W3034236957
Cited by: 242
References: 38
Citations by year: 2026: 13 · 2025: 48 · 2024: 54 · 2023: 35 · 2022: 52 · 2021: 34 · 2020: 6
FWCI: 9.79
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
Keywords: 3D object detection, LiDAR point cloud, voxelization, PointNet, multi-scale feature fusion, autonomous driving, real-time inference, small object detection

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HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection

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