{ "id": "2106.04779", "version": "v1", "published": "2021-06-09T02:58:42.000Z", "updated": "2021-06-09T02:58:42.000Z", "title": "Point Cloud Upsampling via Disentangled Refinement", "authors": [ "Ruihui Li", "Xianzhi Li", "Pheng-Ann Heng", "Chi-Wing Fu" ], "comment": "CVPR 2021, website https://liruihui.github.io/", "categories": [ "cs.CV" ], "abstract": "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.", "revisions": [ { "version": "v1", "updated": "2021-06-09T02:58:42.000Z" } ], "analyses": { "keywords": [ "point cloud upsampling", "spatial refiner", "disentangled refinement", "coarse output", "dense point set" ], "tags": [ "github project" ], "note": { "typesetting": "TeX", "pages": 0, "language": "en", "license": "arXiv", "status": "editable" } } }