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arXiv · 2111.12905

CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-scale Indoor Scene

Haoxiang Chen·Jiahui Huang·Tai-Jiang Mu·Shi-Min Hu
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Abstract

We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions. It is based on an end-to-end sparse convolutional network, CircNet, that jointly models local geometric details and global scene structural contexts, allowing it to preserve fine-grained object detail while recovering missing regions commonly arising in traditional 3D scene data. A novel differentiable rendering module enables test-time refinement for better reconstruction quality. Extensive experiments on both real-world and synthetic datasets show that our concise framework is efficient and effective, achieving better reconstruction quality than the closest competitor while being 10-50x faster.

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Haoxiang Chen, Jiahui Huang, Tai-Jiang Mu, Shi-Min Hu. 2021-11-25. CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-scale Indoor Scene. https://arxiv.org/abs/2111.12905

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