Point Cloud Upsampling via Disentangled Refinement

• URL: http://arxiv.org/abs/2106.04779v1
• Date: Wed, 9 Jun 2021 02:58:42 GMT
• Title: Point Cloud Upsampling via Disentangled Refinement
• Authors: Ruihui Li, Xianzhi Li, Pheng-Ann Heng, and Chi-Wing Fu
• Abstract summary: 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. We formulate two cascaded sub-networks, a dense generator and a spatial refiner.
• Score: 86.3641957163818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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