ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion

• URL: http://arxiv.org/abs/2104.09587v1
• Date: Mon, 19 Apr 2021 19:42:42 GMT
• Title: ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion
• Authors: Yaqi Xia, Yan Xia, Wei Li, Rui Song, Kailang Cao, Uwe Stilla
• Abstract summary: We propose a novel point cloud completion network using a feature matching strategy, termed as ASFM-Net. Specifically, the asymmetrical Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, which can capture detailed shape prior. Experiments are conducted on the PCN dataset and the Completion3D benchmark, demonstrating the state-of-the-art performance of the proposed ASFM-Net.
• Score: 11.243242995190082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle the problem of object completion from point clouds and propose a novel point cloud completion network using a feature matching strategy, termed as ASFM-Net. Specifically, the asymmetrical Siamese auto-encoder neural network is adopted to map the partial and complete input point cloud into a shared latent space, which can capture detailed shape prior. Then we design an iterative refinement unit to generate complete shapes with fine-grained details by integrating prior information. Experiments are conducted on the PCN dataset and the Completion3D benchmark, demonstrating the state-of-the-art performance of the proposed ASFM-Net. The codes and trained models will be open-sourced.

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