The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
Abstract
Point clouds captured in real-world applications are of-ten incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many practical applications. In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion. By rep-resenting the point cloud as a set of unordered groups of points with position embeddings, we convert the point cloud to a sequence of point proxies and employ the transformers for point cloud generation. To facilitate transformers to better leverage the inductive bias about 3D geometric structures of point clouds, we further devise a geometry-aware block that models the local geometric relationships explicitly. The migration of transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Furthermore, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect the real-world scenarios to promote future research. Experimental results show that our method outperforms state-of-the-art methods by a large margin on both the new bench-marks and the existing ones. Code is available at https://github.com/yuxumin/PoinTr.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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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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Self-citationVoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Cited by
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Rao, Y., Zhao, W., Liu, B., Lu, J., Zhou, J., & Hsieh, C.-J. (2021) · arXiv · doi:10.48550/arxiv.2106.02034 · Cited by 448
“Recent progress has demonstrated the variants of transformers can also be a competitive alternative to CNNs and achieve promising results on different vision tasks including image classification [8, 25, 20, 35, 23], object detection [2], semantic segmentation [34, 5] and 3D analysis [31, 33].”
BackgroundSelf-citation
Metadata
- Authors
- Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., & Zhou, J.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 12478-12487
- OpenAlex
- W3203898101
- Cited by
- 544
- References
- 43
- FWCI
- 63.78
- Type
- conference paper
- Language
- en
- Open access
- closed
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