Article
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3D Reconstruction of Indoor Scenes Based on Neural Implicit
Version 1
: Received: 28 August 2024 / Approved: 28 August 2024 / Online: 29 August 2024 (03:13:26 CEST)
How to cite: Lin, Z.; Huang, Y.; Yao, L. 3D Reconstruction of Indoor Scenes Based on Neural Implicit. Preprints 2024, 2024082075. https://doi.org/10.20944/preprints202408.2075.v1 Lin, Z.; Huang, Y.; Yao, L. 3D Reconstruction of Indoor Scenes Based on Neural Implicit. Preprints 2024, 2024082075. https://doi.org/10.20944/preprints202408.2075.v1
Abstract
Reconstructing 3D indoor scenes from 2D images has always been an important task in computer vision and graphics applications. For indoor scenes, traditional 3D reconstruction methods suffer from the lack of surface details, poor reconstruction of large flat surface textures and areas with uneven lighting, and many falsely reconstructed floating debris noises in the reconstructed models. We add adaptive normal priors to the neural implicit reconstruction process to optimize the network, and improve the accuracy of volume density prediction by adding regularization terms to the neural radiation field to constrain the volume density obtained by weight distribution, and learn a smooth SDF surface from the network to obtain an explicit mesh model. Experiments show that the method proposed in this paper outperforms the state-of-the-art methods on ScanNet, Hypersim, and Replica datasets.
Keywords
3D reconstruction; indoor scene; neural radiance fields; signed distance function; normal prior; mesh
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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