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.
翻译:为多视图 3D 重建而最近神经隐含表面的进展主要侧重于提高大规模表面重建的准确性,但往往产生缺乏精细表面细节的超移动的地形。为了解决这个问题,我们提出了一种新型神经隐含表面重建方法,即高分辨率Neus(HR-NeuS),这是一种神经隐含表面重建方法,在保持大规模重建准确性的同时恢复高频表面几何。我们通过(一) 多分辨率散射电网编码,而不是高频率的定位编码来实现这一目标,这提高了我们模型对当地几何细节的清晰度;(二) 粗皮至精度算法框架,有选择地将表面正规化应用于粗糙的几何方法,而没有细细细的细微细节;(三) 粗度至松动电网的反射战略,以训练网络。我们通过在DTU和BlendMVS数据集方面的实验来证明,我们的方法产生了3D的地理分布与以往方法相比,质量更详细和数量性更相似的准确性。