We present Neural Strands, a novel learning framework for modeling accurate
hair geometry and appearance from multi-view image inputs. The learned hair
model can be rendered in real-time from any viewpoint with high-fidelity
view-dependent effects. Our model achieves intuitive shape and style control
unlike volumetric counterparts. To enable these properties, we propose a novel
hair representation based on a neural scalp texture that encodes the geometry
and appearance of individual strands at each texel location. Furthermore, we
introduce a novel neural rendering framework based on rasterization of the
learned hair strands. Our neural rendering is strand-accurate and anti-aliased,
making the rendering view-consistent and photorealistic. Combining appearance
with a multi-view geometric prior, we enable, for the first time, the joint
learning of appearance and explicit hair geometry from a multi-view setup. We
demonstrate the efficacy of our approach in terms of fidelity and efficiency
for various hairstyles.

本文提出了一种名为 Neural Strands 的新型学习框架，它可以从多视角图像输入中准确地建模头发的几何形状和外观，并提出了基于神经头皮纹理的新型头发表示和基于光栅化的新型神经渲染框架，可以同时实现外观和外显头发几何学的联合学习。

神经串：从多视图图像中学习头发的几何和外貌

Neural Strands: Learning Hair Geometry and Appearance from Multi-View  Images

Reconstructing 3D indoor scenes from 2D images is an important task in many
computer vision and graphics applications. A main challenge in this task is
that large texture-less areas in typical indoor scenes make existing methods
struggle to produce satisfactory reconstruction results. We propose a new
method, named NeuRIS, for high quality reconstruction of indoor scenes. The key
idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in
a neural rendering framework for reconstructing large texture-less shapes and,
importantly, to do this in an adaptive manner to also enable the reconstruction
of irregular shapes with fine details. Specifically, we evaluate the
faithfulness of the normal priors on-the-fly by checking the multi-view
consistency of reconstruction during the optimization process. Only the normal
priors accepted as faithful will be utilized for 3D reconstruction, which
typically happens in the regions of smooth shapes possibly with weak texture.
However, for those regions with small objects or thin structures, for which the
normal priors are usually unreliable, we will only rely on visual features of
the input images, since such regions typically contain relatively rich visual
features (e.g., shade changes and boundary contours). Extensive experiments
show that NeuRIS significantly outperforms the state-of-the-art methods in
terms of reconstruction quality.

提出了一种名为 NeuRIS 的新方法，该方法在神经成像框架中整合室内场景的估计法线作为优先重建大型无纹理形状，以及使异形形状具有细节；只有经过验证为忠实的优先权才会用于 3D 重建，实验证明，该方法在重建质量方面显著优于现有的方法。