Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate surface geometry. Hybrid methods alleviate this issue by optimizing the density based on an underlying SDF. However, current SDF methods are overly smooth and miss fine geometric details. In this work, we combine the strengths of these two lines of work in a novel hash-based implicit surface representation. We propose improvements to the two areas by replacing the voxel hash encoding with a permutohedral lattice which optimizes faster, especially for higher dimensions. We additionally propose a regularization scheme which is crucial for recovering high-frequency geometric detail. We evaluate our method on multiple datasets and show that we can recover geometric detail at the level of pores and wrinkles while using only RGB images for supervision. Furthermore, using sphere tracing we can render novel views at 30 fps on an RTX 3090. Code is publicly available at: https://radualexandru.github.io/permuto_sdf

翻译：神经辐射-密度场方法在新视角渲染任务中日益流行。其近期向哈希位置编码的扩展确保了快速训练与推理，且能获得视觉上令人满意的结果。然而，基于密度的方法难以恢复精确的曲面几何。混合方法通过在底层有符号距离函数（SDF）基础上优化密度来缓解这一问题，但现有SDF方法过度平滑，缺失精细几何细节。本研究融合这两类方法的优势，提出一种新颖的基于哈希的隐式曲面表示。我们对两个领域均提出改进：用优化更快（尤其在高维情况下）的置换面体网格替代体素哈希编码，并设计一种对恢复高频几何细节至关重要的正则化方案。我们在多个数据集上评估了所提方法，结果表明仅使用RGB图像作为监督即可恢复毛孔和皱纹级别的几何细节。此外，通过球体追踪，我们可在RTX 3090上以30帧/秒的速率渲染新视角。代码开源地址：https://radualexandru.github.io/permuto_sdf