We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality.
翻译:我们提出了一种新型神经表面重建方法,称为神经表面重建工程,直接从一组2D图像中重建室内图像。最近,隐含神经表层由于高质量的结果和简单性而成为从多视图图像中重建表面的一个有希望的方法。然而,隐含神经表层由于质量高和简洁性而成为从多视图图像中重建表面的一个有希望的方法。然而,隐含神经表层由于受到严重的外形辐射模糊性的影响,通常无法很好地重建室内表面。我们假设室内场面由素质丰富和平坦的无纹质区域组成。在质素丰富的区域,多视图立体立体立体可以取得准确的结果。在平面地区,正常的估算网络通常会得到正常的估计。此外,我们提议一种平滑的方法,根据上述观察,通过可靠的几度前的表面表面表面表面表面表面显示空间变化范围来缩小空间变化范围范围,与此同时,我们用平坦度的图像区域进行平坦度的精确度重建。我们提议一种平坦的深度的系统重建方法,在平坦的地面上对平坦性区域进行更精确的图像进行更精确的复制。