The reconstruction of indoor scenes from multi-view RGB images is challenging
due to the coexistence of flat and texture-less regions alongside delicate and
fine-grained regions. Recent methods leverage neural radiance fields aided by
predicted surface normal priors to recover the scene geometry. These methods
excel in producing complete and smooth results for floor and wall areas.
However, they struggle to capture complex surfaces with high-frequency
structures due to the inadequate neural representation and the inaccurately
predicted normal priors. To improve the capacity of the implicit
representation, we propose a hybrid architecture to represent low-frequency and
high-frequency regions separately. To enhance the normal priors, we introduce a
simple yet effective image sharpening and denoising technique, coupled with a
network that estimates the pixel-wise uncertainty of the predicted surface
normal vectors. Identifying such uncertainty can prevent our model from being
misled by unreliable surface normal supervisions that hinder the accurate
reconstruction of intricate geometries. Experiments on the benchmark datasets
show that our method significantly outperforms existing methods in terms of
reconstruction quality.

通过将低频和高频区域分别表示，并结合图像锐化和去噪技术以及估计像素级表面法线向量的网络，我们的混合架构和改进的正常先验可以显著提高室内场景的重建质量。

室内场景重建与细节增强的混合表达和法线先验

Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid  Representation and Normal Prior Enhancement

Semi-Global Matching (SGM) is a widely-used efficient stereo matching
technique. It works well for textured scenes, but fails on untextured slanted
surfaces due to its fronto-parallel smoothness assumption. To remedy this
problem, we propose a simple extension, termed SGM-P, to utilize precomputed
surface orientation priors. Such priors favor different surface slants in
different 2D image regions or 3D scene regions and can be derived in various
ways. In this paper we evaluate plane orientation priors derived from stereo
matching at a coarser resolution and show that such priors can yield
significant performance gains for difficult weakly-textured scenes. We also
explore surface normal priors derived from Manhattan-world assumptions, and we
analyze the potential performance gains using oracle priors derived from
ground-truth data. SGM-P only adds a minor computational overhead to SGM and is
an attractive alternative to more complex methods employing higher-order
smoothness terms.

本文提出了一种名为 SGM-P 的方法，通过利用表面方向先验来解决超平面光滑性假设对斜面的匹配失败问题，并展示了基于平面和面向曼哈顿世界的法线先验对弱纹理场景的性能收益