We present SIDER(Single-Image neural optimization for facial geometric DEtail
Recovery), a novel photometric optimization method that recovers detailed
facial geometry from a single image in an unsupervised manner. Inspired by
classical techniques of coarse-to-fine optimization and recent advances in
implicit neural representations of 3D shape, SIDER combines a geometry prior
based on statistical models and Signed Distance Functions (SDFs) to recover
facial details from single images. First, it estimates a coarse geometry using
a morphable model represented as an SDF. Next, it reconstructs facial geometry
details by optimizing a photometric loss with respect to the ground truth
image. In contrast to prior work, SIDER does not rely on any dataset priors and
does not require additional supervision from multiple views, lighting changes
or ground truth 3D shape. Extensive qualitative and quantitative evaluation
demonstrates that our method achieves state-of-the-art on facial geometric
detail recovery, using only a single in-the-wild image.

提出了一种无须多张图片和监督的图片光度优化方法 ——SIDER，该方法通过统计模型和符号距离函数结合，利用隐式神经表示的三维形状估计出含有细节的粗糙人脸三维几何模型，并通过光度损失函数优化得到真实图像，实现从单张图片中自动恢复面部几何细节，达到了最先进的效果。

SIDER: 面部几何细节恢复的单图像神经优化

SIDER: Single-Image Neural Optimization for Facial Geometric Detail  Recovery

Direct methods have shown promise on visual odometry and SLAM, leading to
greater accuracy and robustness over feature-based methods. However, offline
3-d reconstruction from internet images has not yet benefited from a joint,
photometric optimization over dense geometry and camera parameters. Issues such
as the lack of brightness constancy, and the sheer volume of data, make this a
more challenging task. This work presents a framework for jointly optimizing
millions of scene points and hundreds of camera poses and intrinsics, using a
photometric cost that is invariant to local lighting changes. The improvement
in metric reconstruction accuracy that it confers over feature-based bundle
adjustment is demonstrated on the large-scale Tanks & Temples benchmark. We
further demonstrate qualitative reconstruction improvements on an internet
photo collection, with challenging diversity in lighting and camera intrinsics.

本文提出一个通过联合光度优化密集几何和摄像机参数，用于 3D 重建的框架，使其比特征点 bundle 调整更具指标重建精度，该框架可以应用于大规模的数据集，并且在面对不同的光照和相机内参时有更好的表现。