The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
SoftGroup for 3D Instance Segmentation on Point Clouds
Open accessAbstract
Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of$+6.2\%$on the ScanNet v2 hidden test set and$+6.8\%$on S3DIS Area 5 in terms of$AP_{50}$. Soft-Group is also fast, running at 345ms per scan with a sin-gle Titan X on ScanNet v2 dataset. The source code and trained models for both datasets are available at https://github.com/thangvubk/SoftGroup.git.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
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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
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368
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VoxNet: A 3D Convolutional Neural Network for real-time object recognition
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KPConv: Flexible and Deformable Convolution for Point Clouds
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Cited by
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Yeshwanth, C., Liu, Y.-C., Nießner, M., & Dai, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00008 · Cited by 316
ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding
Xue, L., Yu, N., Zhang, S., Panagopoulou, A., Li, J., Martín-Martín, R., Wu, J., Xiong, C., Xu, R., Niebles, J. C., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02558 · Cited by 144
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
Ding, R., Yang, J., Xue, C., Zhang, W., Bai, S., & Qi, X. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00677 · Cited by 131
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
Ding, R., Yang, J., Xue, C., Zhang, W., Bai, S., & Qi, X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3410324 · Cited by 34
Metadata
- Authors
- Vu, T., Kim, K., Luu, T. M., Nguyen, T., & Yoo, C. D.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 2698-2707
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312274934
- Cited by
- 279
- References
- 46
- FWCI
- 47.78
- Type
- conference paper
- Language
- en
- Open access
- green
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