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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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DO-SA&R: Distant Object Augmented Set Abstraction and Regression for Point-Based 3D Object Detection
He, X., Wang, Z., Lin, J., Nai, K., Yuan, J., & Li, Z. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3326394 · Cited by 16
Tiong, L. C. O., Yoo, H. J., Kim, N., Kim, C., Lee, K., Han, S. S., & Kim, D.-H. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01216-7 · Cited by 16
Zhang, H., Liu, L., Huang, Y., Yang, Z., Lei, X., & Wen, B. (2024) · 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/cvpr52733.2024.01452 · Cited by 16
Sparse agent transformer for unified voxel and image feature extraction and fusion
Zhang, H., Wan, J., He, Z., Song, J., Yang, Y. F., & Yuan, D. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102455 · Cited by 14
A review of 3D object detection based on autonomous driving
Wang, H., Chen, X., Yuan, Q., & Liu, P. (2024) · The Visual Computer · doi:10.1007/s00371-024-03480-6 · Cited by 17
RAE3D: Multiscale Aggregation-Enhanced 3D Object Detection for Rail Transit Obstacle Perception
Lian, L., Cao, Z., Qin, Y., Gao, Y., Li, W., Bai, J., Ge, X., & Yang, T. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3540494 · Cited by 15
Liu, K., Zhao, Y., Gao, Z. Y., & Chen, B. M. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811959 · Cited by 15
HP3D-V2V: High-Precision 3D Object Detection Vehicle-to-Vehicle Cooperative Perception Algorithm
Chen, H., Wang, H., Liu, Z., Gu, D., & Ye, W. (2024) · Sensors · doi:10.3390/s24072170 · Cited by 14
Shao, Y., Tan, A. C., Wang, B., Yan, T., Sun, Z., Zhang, Y., & Liu, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106623 · Cited by 15
Snow-CLOCs: Camera-LiDAR Object Candidate Fusion for 3D Object Detection in Snowy Conditions
Fan, X., Xiao, D., Li, Q., & Gong, R. (2024) · Sensors · doi:10.3390/s24134158 · Cited by 13
PointDrop: Improving Object Detection from Sparse Point Clouds via Adversarial Data Augmentation
Ma, W., Chen, J., Du, Q., & Jia, W. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412691 · Cited by 15
LossDistillNet: 3D Object Detection in Point Cloud Under Harsh Weather Conditions
Do, A. T., & Yoo, M. (2022) · IEEE Access · doi:10.1109/access.2022.3197765 · Cited by 13
Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
Köhler, D., Quach, M., Ulrich, M., Meinl, F., Bischoff, B., & Blume, H. (2023) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion52260.2023.10224223 · Cited by 13
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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