Exploiting Multiple Priors for Neural 3D Indoor Reconstruction

• URL: http://arxiv.org/abs/2309.07021v1
• Date: Wed, 13 Sep 2023 15:23:43 GMT
• Title: Exploiting Multiple Priors for Neural 3D Indoor Reconstruction
• Authors: Federico Lincetto, Gianluca Agresti, Mattia Rossi, Pietro Zanuttigh
• Abstract summary: We propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments. Experimental results show that our approach produces state-of-the-art 3D reconstructions in challenging indoor scenarios.
• Score: 15.282699095607594
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Neural implicit modeling permits to achieve impressive 3D reconstruction results on small objects, while it exhibits significant limitations in large indoor scenes. In this work, we propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor environments, while relying only on images. A sparse but accurate depth prior is used to anchor the scene to the initial model. A dense but less accurate depth prior is also introduced, flexible enough to still let the model diverge from it to improve the estimated geometry. Then, a novel self-supervised strategy to regularize the estimated surface normals is presented. Finally, a learnable exposure compensation scheme permits to cope with challenging lighting conditions. Experimental results show that our approach produces state-of-the-art 3D reconstructions in challenging indoor scenarios.

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