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已经成为新视点渲染任务中越来越流行的方法。它们最近的扩展到基于哈希的位置编码,保证了快速的训练和推理,同时具有视觉效果良好的结果。然而,基于密度的方法在恢复准确的表面几何方面存在问题。混合方法通过基于潜在SDF的优化来缓解这个问题。然而,当前的SDF方法过于光滑,无法捕捉到细粒度的几何细节。在这项工作中,我们结合了这两个工作领域的优势,提出了一种新的基于哈希的隐式曲面表示方法。我们通过使用permutohedral lattice替换体素哈希编码来提出改进,对于更高的维度,其优化速度更快。我们还提出了一种正则化方法,这对于恢复高频几何细节至关重要。我们在多个数据集上评估了我们的方法,证明了我们可以在只使用RGB图像进行监督的情况下恢复毛孔和皱纹的细节。此外,使用球追踪,我们可以在RTX 3090上以30fps渲染新视角。代码公开提供在:https://radualexandru.github.io/permuto_sdf