SPCNet: Stepwise Point Cloud Completion Network
How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task. We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings. SPCNet has a hierarchical bottom-to-up network architecture. It fulfills shape completion in an iterative manner, which 1) first infers the global feature of the coarse result; 2) then infers the local feature with the aid of global feature; and 3) finally infers the detailed result with the help of local feature and coarse result. Beyond the wisdom of simulating the physical repair, we newly design a cycle loss %based training strategy to enhance the generalization and robustness of SPCNet. Extensive experiments clearly show the superiority of our SPCNet over the state-of-the-art methods on 3D point clouds with large missings.
Code (4)
Tasks
Point Cloud CompletionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Semantic Point Completion Network for 3D Semantic Scene Completion
Semantic scene completion (SSC) is composed of scene completion (SC) and semantic segmentation. Most of the existing methods carry out SSC in a regular 3D grid space, where 3D CNNs cause unnecessary computational cost…
3D Semantic Scene CompletionDecoderSemantic SegmentationUnsupervised Point Cloud Completion through Unbalanced Optimal Transport
Unpaired point cloud completion explores methods for learning a completion map from unpaired incomplete and complete point cloud data. In this paper, we propose a novel approach for unpaired point cloud completion using …
Point Cloud CompletionPF-Net: Point Fractal Network for 3D Point Cloud Completion
In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based approach for precise and high-fidelity point cloud completion. Unlike existing point cloud completion networks, which generate the overal…
Point Cloud CompletionRefComp: A Reference-guided Unified Framework for Unpaired Point Cloud Completion
The unpaired point cloud completion task aims to complete a partial point cloud by using models trained with no ground truth. Existing unpaired point cloud completion methods are class-aware, i.e., a separate model is ne…
Point Cloud CompletionTranslationSPoVT: Semantic Prototype Variational Transformer for Dense Point Cloud Semantic Completion
Point cloud completion is an active research topic for 3D vision and has been widely studied in recent years. Instead of directly predicting the missing point cloud from the partial input, we introduce a Semantic-Prototy…
Point Cloud CompletionPoint Cloud Semantic Completion