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|a 10.1109/TPAMI.2023.3309253
|2 doi
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|a eng
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1 |
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|a Yu, Xumin
|e verfasserin
|4 aut
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| 245 |
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|a AdaPoinTr
|b Diverse Point Cloud Completion With Adaptive Geometry-Aware Transformers
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|c 2023
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|a Text
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|a Date Revised 07.11.2023
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we propose a Transformer encoder-decoder architecture, called PoinTr, which reformulates point cloud completion as a set-to-set translation problem and employs a geometry-aware block to model local geometric relationships explicitly. The migration of Transformers enables our model to better learn structural knowledge and preserve detailed information for point cloud completion. Taking a step towards more complicated and diverse situations, we further propose AdaPoinTr by developing an adaptive query generation mechanism and designing a novel denoising task during completing a point cloud. Coupling these two techniques enables us to train the model efficiently and effectively: we reduce training time (by 15x or more) and improve completion performance (over 20%). Additionally, we propose two more challenging benchmarks with more diverse incomplete point clouds that can better reflect real-world scenarios to promote future research. We also show our method can be extended to the scene-level point cloud completion scenario by designing a new geometry-enhanced semantic scene completion framework. Extensive experiments on the existing and newly-proposed datasets demonstrate the effectiveness of our method, which attains 6.53 CD on PCN, 0.81 CD on ShapeNet-55 and 0.392 MMD on real-world KITTI, surpassing other work by a large margin and establishing new state-of-the-arts on various benchmarks. Most notably, AdaPoinTr can achieve such promising performance with higher throughputs and fewer FLOPs compared with the previous best methods in practice
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|a Journal Article
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|a Rao, Yongming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Ziyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Jiwen
|e verfasserin
|4 aut
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| 700 |
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|a Zhou, Jie
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 12 vom: 01. Dez., Seite 14114-14130
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|g volume:45
|g year:2023
|g number:12
|g day:01
|g month:12
|g pages:14114-14130
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|u http://dx.doi.org/10.1109/TPAMI.2023.3309253
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