Point Cloud Upsampling via Disentangled Refinement
This work addresses point cloud upsampling for 3D scanning applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of upsampling sparse, non-uniform, and noisy 3D point clouds by proposing a method that disentangles the task into dense generation and spatial refinement, achieving state-of-the-art results on synthetic and real-scanned datasets.
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner. The dense generator infers a coarse but dense output that roughly describes the underlying surface, while the spatial refiner further fine-tunes the coarse output by adjusting the location of each point. Specifically, we design a pair of local and global refinement units in the spatial refiner to evolve a coarse feature map. Also, in the spatial refiner, we regress a per-point offset vector to further adjust the coarse outputs in fine-scale. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-arts.