View-Guided Point Cloud Completion
Xuancheng Zhang, Yutong Feng, Siqi Li, Changqing Zou, Hai Wan, Xibin Zhao, Yandong Guo, Yue Gao
Abstract
This paper presents a view-guided solution for the task of point cloud completion. Unlike most existing methods directly inferring the missing points using shape priors, we address this task by introducing ViPC (view-guided point cloud completion) that takes the missing crucial global structure information from an extra single-view image. By leveraging a framework that sequentially performs effective cross-modality and cross-level fusions, our method achieves significantly superior results over typical existing solutions on a new large-scale dataset we collect for the view-guided point cloud completion task.
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Install the CLIlune papers fulltext 2be9d19d-662b-44c2-a9d6-e0b1b3aeba08Cited by top-tier papers25
- PointAttN: You Only Need Attention for Point Cloud CompletionJun Wang, Ying Cui, Dongyan Guo, Junxia Li et al.AAAI 2024 · 113 citations
- Cross-modal Learning for Image-Guided Point Cloud Shape CompletionEmanuele Aiello, Diego Valsesia, Enrico MagliNeurIPS 2022 · 82 citations
- SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generatorZhe Zhu, Honghua Chen, Xing He, Weiming Wang et al.ICCV 2023 · 59 citations
- Aggregating Feature Point Cloud for Depth CompletionZhu Yu, Zehua Sheng, Zili Zhou, Lun Luo et al.ICCV 2023 · 42 citations
- CDPNet: Cross-Modal Dual Phases Network for Point Cloud CompletionZhenjiang Du, Jiale Dou, Zhitao Liu, Jiwei Wei et al.AAAI 2024 · 17 citations
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