PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling
This work improves 3D reconstruction for applications like robotics and computer vision, though it is incremental as it builds on existing patch-based upsampling techniques.
The paper tackles the problem of point cloud upsampling by addressing the lack of global spatial consistency in existing patch-based methods, resulting in a novel approach that outperforms previous methods, especially with noisy inputs.
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface. Existing methods divide the input points into small patches and upsample each patch separately, however, ignoring the global spatial consistency between patches. In this paper, we present a novel method PC$^2$-PU, which explores patch-to-patch and point-to-point correlations for more effective and robust point cloud upsampling. Specifically, our network has two appealing designs: (i) We take adjacent patches as supplementary inputs to compensate the loss structure information within a single patch and introduce a Patch Correlation Module to capture the difference and similarity between patches. (ii) After augmenting each patch's geometry, we further introduce a Point Correlation Module to reveal the relationship of points inside each patch to maintain the local spatial consistency. Extensive experiments on both synthetic and real scanned datasets demonstrate that our method surpasses previous upsampling methods, particularly with the noisy inputs. The code and data are at \url{https://github.com/chenlongwhu/PC2-PU.git}.