RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency

• URL: http://arxiv.org/abs/2310.19629v2
• Date: Sun, 17 Dec 2023 01:19:13 GMT
• Title: RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency
• Authors: Zhuoman Liu, Bo Yang, Yan Luximon, Ajay Kumar, Jinxi Li
• Abstract summary: We propose a new framework called RayDF to formulate 3D shapes as ray-based neural functions. Our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image.
• Score: 10.55497978011315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shapes as ray-based neural functions, but the learned structures are inferior due to the lack of multi-view geometry consistency. To tackle these challenges, we propose a new framework called RayDF. It consists of three major components: 1) the simple ray-surface distance field, 2) the novel dual-ray visibility classifier, and 3) a multi-view consistency optimization module to drive the learned ray-surface distances to be multi-view geometry consistent. We extensively evaluate our method on three public datasets, demonstrating remarkable performance in 3D surface point reconstruction on both synthetic and challenging real-world 3D scenes, clearly surpassing existing coordinate-based and ray-based baselines. Most notably, our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image, showing the superiority of our method for 3D shape representation. Our code and data are available at https://github.com/vLAR-group/RayDF

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