In this paper, we study the problem of continuous 3D shape representations.
The majority of existing successful methods are coordinate-based implicit
neural representations. However, they are inefficient to render novel views or
recover explicit surface points. A few works start to formulate 3D shapes as
ray-based neural functions, but the learned structures are inferior due to the
lack of multi-view geometry consistency. To tackle these challenges, we propose
a new framework called RayDF. It consists of three major components: 1) the
simple ray-surface distance field, 2) the novel dual-ray visibility classifier,
and 3) a multi-view consistency optimization module to drive the learned
ray-surface distances to be multi-view geometry consistent. We extensively
evaluate our method on three public datasets, demonstrating remarkable
performance in 3D surface point reconstruction on both synthetic and
challenging real-world 3D scenes, clearly surpassing existing coordinate-based
and ray-based baselines. Most notably, our method achieves a 1000x faster speed
than coordinate-based methods to render an 800x800 depth image, showing the
superiority of our method for 3D shape representation. Our code and data are
available at this https URL

本文研究了连续三维形状表示的问题，提出了一种名为 RayDF 的新框架，包括简单的光线 - 表面距离场、新颖的双光线可见性分类器和多视图一致性优化模块，通过在三个公共数据集上的广泛评估，证明了该方法在合成和真实的三维场景中对三维表面点重建的显著性能，超越了现有的基于坐标和基于光线的基准，特别地，在渲染 800x800 深度图像时，与基于坐标的方法相比，我们的方法速度提高了 1000 倍，展示了其在三维形状表示中的优越性。

RayDF：多视角一致性的神经光线 - 曲面距离场

RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency

Recent learning-based approaches, in which models are trained by single-view
images have shown promising results for monocular 3D face reconstruction, but
they suffer from the ill-posed face pose and depth ambiguity issue. In contrast
to previous works that only enforce 2D feature constraints, we propose a
self-supervised training architecture by leveraging the multi-view geometry
consistency, which provides reliable constraints on face pose and depth
estimation. We first propose an occlusion-aware view synthesis method to apply
multi-view geometry consistency to self-supervised learning. Then we design
three novel loss functions for multi-view consistency, including the pixel
consistency loss, the depth consistency loss, and the facial landmark-based
epipolar loss. Our method is accurate and robust, especially under large
variations of expressions, poses, and illumination conditions. Comprehensive
experiments on the face alignment and 3D face reconstruction benchmarks have
demonstrated superiority over state-of-the-art methods. Our code and model are
released in this https URL

本文提出了基于多视角几何一致的自监督训练架构，目标是在面部姿态和深度估计过程中提供可靠的约束。实验结果表明，该方法在面部姿态、表情和光照等方面的变异下都表现出优越性。