The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Group-Free 3D Object Detection via Transformers
Open accessAbstract
Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object representation from an irregular point cloud, existing methods usually take a point grouping step to assign the points to an object candidate so that a PointNet-like network could be used to derive object features from the grouped points. However, the inaccurate point assignments caused by the hand-crafted grouping scheme decrease the performance of 3D object detection.In this paper, we present a simple yet effective method for directly detecting 3D objects from the 3D point cloud. Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers [42], where the contribution of each point is automatically learned in the network training. With an improved attention stacking scheme, our method fuses object features in different stages and generates more accurate object detection results. With few bells and whistles, the proposed method achieves state-of-the-art 3D object detection performance on two widely used benchmarks, Scan-Net V2 and SUN RGB-D. The code and models are publicly available at https://github.com/zeliu98/Group-Free-3D
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Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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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
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · arXiv · doi:10.48550/arxiv.2005.12872 · Cited by 18,253
RoBERTa: A Robustly Optimized BERT Pretraining Approach
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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3D ShapeNets: A deep representation for volumetric shapes
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TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., & Tai, C.-L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00116 · Cited by 990
Stratified Transformer for 3D Point Cloud Segmentation
Lai, X., Liu, J., Jiang, L., Wang, L., Zhao, H., Liu, S., Qi, X., & Jia, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00831 · Cited by 566
A Survey of Visual Transformers
Liu, Y., Zhang, Y., Wang, Y., Hou, F., Yuan, J., Tian, J., Zhang, Y., Shi, Z., Fan, J., & He, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3227717 · Cited by 549
Multimodal Token Fusion for Vision Transformers
Wang, Y., Chen, X., Cao, L., Huang, W., Sun, F., & Wang, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01187 · Cited by 272
ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding
Xue, L., Gao, M., Xing, C., Martín-Martín, R., Wu, J., Xiong, C., Xu, R., Niebles, J. C., & Savarese, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00120 · Cited by 258
Metadata
- Authors
- Liu, Z., Zhang, Z., Cao, Y., Hu, H., & Tong, X.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 2929-2938
- OpenAlex
- W3143166043
- Cited by
- 298
- References
- 49
- FWCI
- 42.20
- Type
- conference paper
- Language
- en
- Open access
- green
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