The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
Open accessAbstract
We introduce Position Adaptive Convolution (PAConv), a generic convolution operation for 3D point cloud processing. The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet. In this way, the kernel is built in a data-driven manner, endowing PAConv with more flexibility than 2D convolutions to better handle the irregular and unordered point cloud data. Besides, the complexity of the learning process is reduced by combining weight matrices instead of brutally predicting kernels from point positions.Furthermore, different from the existing point convolution operators whose network architectures are often heavily engineered, we integrate our PAConv into classical MLP-based point cloud pipelines without changing network configurations. Even built on simple networks, our method still approaches or even surpasses the state-of-the-art models, and significantly improves baseline performance on both classification and segmentation tasks, yet with decent efficiency. Thorough ablation studies and visualizations are provided to understand PAConv. Code is released on https://github.com/CVMI-Lab/PAConv.
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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
Approximation capabilities of multilayer feedforward networks
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KPConv: Flexible and Deformable Convolution for Point Clouds
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Pearson Correlation Coefficient
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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
Self-citationLowis3D: 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
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Metadata
- Authors
- Xu, M., Ding, R., Zhao, H., & Qi, X.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 3172-3181
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3171433839
- Cited by
- 523
- References
- 60
- FWCI
- 67.79
- Type
- conference paper
- Language
- en
- Open access
- green
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