  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. This work aims to reconstruct high-fidelity surfaces
with fine-grained details by addressing the above limitations. 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
outperforms existing methods in terms of reconstruction quality. Furthermore,
the proposed method also generalizes well to real-world indoor scenarios
captured by our hand-held mobile phones. Our code is publicly available at:
https://github.com/yec22/Fine-Grained-Indoor-Recon.
