Complete point clouds by using missing part sensitive transformer with ProxyFormer
Complete point clouds by using missing part sensitive transformer with ProxyFormer
ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer
arXiv paper abstract https://arxiv.org/abs/2302.14435
arXiv PDF paper https://arxiv.org/pdf/2302.14435.pdf
Problems ... will lead the captured point clouds to be incomplete.
... propose a novel point cloud completion approach namely ProxyFormer that divides point clouds into existing (input) and missing (to be predicted) parts and each part communicates information through its proxies.
... fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies.
... to better perceive the position of missing points ... design a missing part sensitive transformer, which converts random normal distribution into reasonable position information, and uses proxy alignment to refine the missing proxies.
It makes the predicted point proxies more sensitive to the features and positions of the missing part, and thus make these proxies more suitable for subsequent coarse-to-fine processes.
... method outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed ...
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