The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
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]
Citations
Cites
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Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
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(2017) · Cited by 11,625
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Zhou, Y., & Tuzel, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00472 · Cited by 4,813
PointPillars: Fast Encoders for Object Detection From Point Clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01298 · Cited by 4,673
Research works printed but not linked: 1
- paszke, 2017, On Automatic Differentiation
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Sorted by citations
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., & Li, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16207 · Cited by 1,048
Shi, S., Jiang, L., Deng, J., Wang, Z., Guo, C., Shi, J., Wang, X., & Li, H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01710-9 · Cited by 602
Voxel Transformer for 3D Object Detection
Mao, J., Xue, Y., Niu, M., Bai, H., Feng, J., Liang, X., Xu, H., & Xu, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00315 · Cited by 520
Du, G., Wang, K., Lian, S., & Zhao, K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09888-5 · Cited by 553
3D Object Detection with Pointformer
Pan, X., Xia, Z., Song, S., Li, L. E., & Huang, G. M. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00738 · Cited by 505
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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