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HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection
Open accessAbstract
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 [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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BFT3D: A Robust BEV Feature Transformation Module for Multisensor 3-D Object Detection
Liu, B., & Wu, Y. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3580425 · Cited by 1
SAMUNet: Enhancing pillar-based 3D object detection in autonomous driving with Shape-aware Mini-Unet
Zhu, L., Li, X., Li, B., Li, C., Wang, B., & Chang, X. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105703 · Cited by 1
Research on Power Laser Inspection Technology Based on High-Precision Servo Control System
An, Z., & Pei, Y. (2025) · Photonics · doi:10.3390/photonics12090944 · Cited by 1
Direct, Targetless and Automatic Joint Calibration of LiDAR-Camera Intrinsic and Extrinsic
Shen, Y., Hong, S., Shen, S., & Qin, T. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247599 · Cited by 1
Li, J., Peng, K., & Sun, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246382 · Cited by 1
PAPNet: Point-Enhanced Attention-Aware Pillar Network for 3D Object Detection in Autonomous Driving
Li, R., Zhao, Y., Xu, X., Chen, J., Song, R., & Zhang, W. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3653431 · Cited by 1
KNN improved Transformer for 3D object detection
Jiang, C., Lu, S., Zhou, X., Ma, T., & Zhai, J. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117488 · Cited by 1
CenterNext: Learning Generalized Gaussian Representation for LiDAR 3D Object Detection
Dong, H., Zhang, H., Chen, S., Xin, J., & Zheng, N. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423609 · Cited by 1
SpaA: A Spatial-Aware Network for 3D Object Detection from LiDAR Point Clouds
Song, J., Zhang, C. G., Zhang, C. G., Song, L., Wang, R., & Xie, K. (2026) · Remote Sensing · doi:10.3390/rs18081104 · Cited by 1
Metadata
- Authors
- Ye, M., Xu, S., & Cao, T.
- 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)
- 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
- 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 › Computer Science › Computer Graphics and Computer-Aided Design › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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