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Scnet: shape-aware convolution with KFNN for point clouds completion
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DOI：10.1007/s13042-024-02359-1.png)
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Abstract
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Scanned 3D point cloud data is typically noisy and incomplete. Existing point cloud completion methods tend to learn a mapping of available parts to the complete one but ignore the structural relationships in local regions. They are less competent in learning point distributions and recovering the details of the object. This paper proposes a shape-aware point cloud completion network (SCNet) that employs multi-scale features and a coarse-to-fine strategy to generate detailed, complete point clouds. Firstly, we introduce a K-feature nearest neighbor algorithm to explore local geometric structure and design a novel shape-aware graph convolution that utilizes multiple learnable filters to perceive local shape changes in different directions. Secondly, we adopt non-local feature expansion to generate a coarse point cloud as the rough shape and merge it with the input data to preserve the original structure. Finally, we employ a residual network to fine-tune the point coordinates to smooth the merged point cloud, which is then optimized to a fine point cloud using a refinement module with shape-aware graph convolution and local attention mechanisms. Extensive experiments demonstrate that our SCNet outperforms other methods on the same point cloud completion benchmark and is more stable and robust.
Keywords:
Point cloud
Point cloud completion
Shape-aware convolution
Deep learning
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