ME-PCN: Point Completion Conditioned on Mask Emptiness

• URL: http://arxiv.org/abs/2108.08187v1
• Date: Wed, 18 Aug 2021 15:02:27 GMT
• Title: ME-PCN: Point Completion Conditioned on Mask Emptiness
• Authors: Bingchen Gong, Yinyu Nie, Yiqun Lin, Xiaoguang Han, Yizhou Yu
• Abstract summary: Main-stream methods predict missing shapes by decoding a global feature learned from the input point cloud. We present ME-PCN, a point completion network that leverages emptiness' in 3D shape space.
• Score: 50.414383063838336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point completion refers to completing the missing geometries of an object from incomplete observations. Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details. In this work, we present ME-PCN, a point completion network that leverages `emptiness' in 3D shape space. Given a single depth scan, previous methods often encode the occupied partial shapes while ignoring the empty regions (e.g. holes) in depth maps. In contrast, we argue that these `emptiness' clues indicate shape boundaries that can be used to improve topology representation and detail granularity on surfaces. Specifically, our ME-PCN encodes both the occupied point cloud and the neighboring `empty points'. It estimates coarse-grained but complete and reasonable surface points in the first stage, followed by a refinement stage to produce fine-grained surface details. Comprehensive experiments verify that our ME-PCN presents better qualitative and quantitative performance against the state-of-the-art. Besides, we further prove that our `emptiness' design is lightweight and easy to embed in existing methods, which shows consistent effectiveness in improving the CD and EMD scores.

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