The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation
Abstract
Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometrically accurate, but it is disordered, which makes it difficult to find local neighbors efficiently. The voxel-based view is regular, but sparse, and computation grows cubicly when voxel resolution increases. The range-based view is regular and generally dense, however spherical projection makes physical dimensions distorted. Both voxel-and range-based views suffer from quantization loss, especially for voxels when facing large-scale scenes. In order to utilize different view’s advantages and alleviate their own shortcomings in fine-grained segmentation task, we propose a novel range-point-voxel fusion network, namely RPVNet. In this network, we devise a deep fusion framework with multiple and mutual information interactions among these three views, and propose a gated fusion module (termed as GFM), which can adaptively merge the three features based on concurrent inputs. Moreover, the proposed RPV interaction mechanism is highly efficient, and we summarize it to a more general formulation. By leveraging this efficient interaction and relatively lower voxel resolution, our method is also proved to be more efficient. Finally, we evaluated the proposed model on two large-scale datasets, i.e., SemanticKITTI and nuScenes, and it shows state-of-the-art performance on both of them. Note that, our method currently ranks 1st on SemanticKITTI leaderboard without any extra tricks.
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Cites
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., Jindal, A., & Singh, A. K. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,040
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
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
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Maturana, D., & Scherer, S. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353481 · Cited by 3,458
Multi-view 3D Object Detection Network for Autonomous Driving
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Cited by
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
Yan, X., Gao, J., Zheng, C., Zheng, C., Zhang, R., Cui, S., & Li, Z. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_39 · Cited by 280
Self-citationFusionpainting: Multimodal fusion with adaptive attention for 3d object detection
Xu, S., Zhou, D., Fang, J., Yin, J., Bin, Z., & Zhang, L. (2021) · 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc48978.2021.9564951 · Cited by 237
Multi-Sem Fusion: Multimodal Semantic Fusion for 3-D Object Detection
Xu, S., Li, F., Song, Z., Fang, J., Wang, S., & Yang, Z.-X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3387732 · Cited by 26
Metadata
- Authors
- Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., & Pu, S.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 16004-16013
- OpenAlex
- W3137210930
- Cited by
- 336
- References
- 49
- FWCI
- 19.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xu, J., Zhang, R., Dou, J., Zhu, Y., Sun, J., & Pu, S. (2021). RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.01572
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