  Recent advances in neural implicit surfaces for multi-view 3D reconstruction
primarily focus on improving large-scale surface reconstruction accuracy, but
often produce over-smoothed geometries that lack fine surface details. To
address this, we present High-Resolution NeuS (HR-NeuS), a novel neural
implicit surface reconstruction method that recovers high-frequency surface
geometry while maintaining large-scale reconstruction accuracy. We achieve this
by utilizing (i) multi-resolution hash grid encoding rather than positional
encoding at high frequencies, which boosts our model's expressiveness of local
geometry details; (ii) a coarse-to-fine algorithmic framework that selectively
applies surface regularization to coarse geometry without smoothing away fine
details; (iii) a coarse-to-fine grid annealing strategy to train the network.
We demonstrate through experiments on DTU and BlendedMVS datasets that our
approach produces 3D geometries that are qualitatively more detailed and
quantitatively of similar accuracy compared to previous approaches.
