The content discusses the development of the Few-point Shape Completion (FSC) model for 3D object shape completion using sparse point clouds. The FSC model incorporates a dual-branch feature extractor and a two-stage revision network to enhance completion accuracy. Experimental results show significant improvements over previous methods, especially in scenarios with very few input points.
The study highlights the importance of completing point clouds with limited input points and explores the potential of utilizing even a few dozen points for accurate shape recovery. By leveraging entropy analysis and innovative network architectures, the FSC model demonstrates robustness and generalizability across different object categories.
Key contributions include investigating minimum input points required for complete point cloud reconstruction, introducing a novel FSC model tailored for few-point completion tasks, and designing specialized modules for comprehensive feature extraction and revision. The experimental results showcase superior performance in both seen and unseen object categories, emphasizing the effectiveness of the proposed approach.
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arxiv.org
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