Lin Zhaoji, Huang Yutao, Yao Li
School of Computer Science and Engineering, Sanjiang University, Nanjing 210012, China.
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
J Imaging. 2024 Sep 16;10(9):231. doi: 10.3390/jimaging10090231.
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 have problems such as missing surface details, poor reconstruction of large plane textures and uneven illumination areas, and many wrongly reconstructed floating debris noises in the reconstructed models. This paper proposes a 3D reconstruction method for indoor scenes that combines neural radiation field (NeRFs) and signed distance function (SDF) implicit expressions. The volume density of the NeRF is used to provide geometric information for the SDF field, and the learning of geometric shapes and surfaces is strengthened by adding an adaptive normal prior optimization learning process. It not only preserves the high-quality geometric information of the NeRF, but also uses the SDF to generate an explicit mesh with a smooth surface, significantly improving the reconstruction quality of large plane textures and uneven illumination areas in indoor scenes. At the same time, a new regularization term is designed to constrain the weight distribution, making it an ideal unimodal compact distribution, thereby alleviating the problem of uneven density distribution and achieving the effect of floating debris removal in the final model. Experiments show that the 3D reconstruction effect of this paper on ScanNet, Hypersim, and Replica datasets outperforms the state-of-the-art methods.
从二维图像重建三维室内场景一直是计算机视觉和图形应用中的一项重要任务。对于室内场景，传统的三维重建方法存在诸如表面细节缺失、大平面纹理重建不佳、光照区域不均匀以及重建模型中存在许多错误重建的浮动碎片噪声等问题。本文提出了一种结合神经辐射场（NeRFs）和符号距离函数（SDF）隐式表达式的室内场景三维重建方法。NeRF的体密度用于为SDF场提供几何信息，并通过添加自适应法线先验优化学习过程来加强几何形状和表面的学习。它不仅保留了NeRF的高质量几何信息，还利用SDF生成具有光滑表面的显式网格，显著提高了室内场景中大平面纹理和光照不均匀区域的重建质量。同时，设计了一个新的正则化项来约束权重分布，使其成为理想的单峰紧凑分布，从而缓解密度分布不均匀的问题，并在最终模型中实现去除浮动碎片的效果。实验表明，本文在ScanNet、Hypersim和Replica数据集上的三维重建效果优于现有方法。
