VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Open accessAbstract
Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Specifically, VoxelNet divides a point cloud into equally spaced 3D voxels and transforms a group of points within each voxel into a unified feature representation through the newly introduced voxel feature encoding (VFE) layer. In this way, the point cloud is encoded as a descriptive volumetric representation, which is then connected to a RPN to generate detections. Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.
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VoxelNet suffers from substantial computational demands due to its dense voxel representation, hindering its feasibility for real-time implementation.
“However, due to its dense voxel representation, VoxelNet suffers from substantial computational demands, in this way to hinder its feasibility for real-time implementation.”
From Use and Potential of AI in Assisting Surveyors in Building Retrofit and Demolition—A Scoping Review (2025) · cites this paper as [47]
The effectiveness of deep learning components to adapt based on past experiences can be used to learn the parameters of the car's control system, better approximating the underlying true system model.
“The effectiveness of deep learning components to adapt based on past experiences can also be used to learn the parameters of the car's control system, thus better approximating the underlaying true system model [174, 94].”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [174]
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“Despite the remarkable progress of LiDAR-based methods [43, 20, 53, 49, 8], camera-based approaches [45, 32, 47, 30] have attracted extensive attention in recent years.”
MethodGAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection
Li, X., Fan, B., Tian, J., & Fan, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02004 · Cited by 42 · see citing works
“VoxelNet [47] dis-cretizes point clouds into voxels, and uses dense convolution to obtain BEV features.”
MethodPan, C., He, Y., Peng, J., Zhang, Q., Sui, W., & Zhang, Z. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00925 · Cited by 33 · see citing works
Use and Potential of AI in Assisting Surveyors in Building Retrofit and Demolition—A Scoping Review
Yin, Y., Zuo, H., Jennings, T., Jain, S., Cartwright, B., Buhagiar, J., Williams, P., Adams, K., Hazeri, K., & Childs, P. (2025) · Buildings · doi:10.3390/buildings15193448 · Cited by 1 · see citing works
- Authors
- Zhou, Y., & Tuzel, O.
- Affiliations
- Apple (United States) (US)
- ORCID
- Zhou, Yin
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4490-4499
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963727135
- Cited by
- 4,813
- References
- 49
- Citations by year
- 2026: 381 · 2025: 778 · 2024: 854 · 2023: 809 · 2022: 615 · 2021: 590 · 2020: 448 · 2019: 283 · 2018: 53 · 2017: 2
- FWCI
- 103.55
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- 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 › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- 3D object detection, point clouds, LiDAR, region proposal network, end-to-end learning, volumetric representation, pedestrian and cyclist detection, autonomous driving
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