Visual localization techniques rely upon some underlying scene representation
to localize against. These representations can be explicit such as 3D SFM map
or implicit, such as a neural network that learns to encode the scene. The
former requires sparse feature extractors and matchers to build the scene
representation. The latter might lack geometric grounding not capturing the 3D
structure of the scene well enough. This paper proposes to jointly learn the
scene representation along with a 3D dense feature field and a 2D feature
extractor whose outputs are embedded in the same metric space. Through a
contrastive framework we align this volumetric field with the image-based
extractor and regularize the latter with a ranking loss from learned surface
information. We learn the underlying geometry of the scene with an implicit
field through volumetric rendering and design our feature field to leverage
intermediate geometric information encoded in the implicit field. The resulting
features are discriminative and robust to viewpoint change while maintaining
rich encoded information. Visual localization is then achieved by aligning the
image-based features and the rendered volumetric features. We show the
effectiveness of our approach on real-world scenes, demonstrating that our
approach outperforms prior and concurrent work on leveraging implicit scene
representations for localization.

通过在同一度量空间中嵌入图像特征提取器和体积特征场，通过对比框架对齐体积特征与图像特征并利用学习的表面信息对后者进行正则化，本文提出了一种联合学习场景表示、体积渲染和特征场设计的方法，用于视觉定位，并在真实场景中验证了该方法的有效性。

神经隐式特征场的自监督学习用于相机姿态细化

Self-supervised Learning of Neural Implicit Feature Fields for Camera  Pose Refinement

Recovering the physical attributes of an object's appearance from its images
captured under an unknown illumination is challenging yet essential for
photo-realistic rendering. Recent approaches adopt the emerging implicit scene
representations and have shown impressive results.However, they unanimously
adopt a surface-based representation,and hence can not well handle scenes with
very complex geometry, translucent object and etc. In this paper, we propose to
conduct inverse volume rendering, in contrast to surface-based, by representing
a scene using microflake volume, which assumes the space is filled with
infinite small flakes and light reflects or scatters at each spatial location
according to microflake distributions. We further adopt the coordinate networks
to implicitly encode the microflake volume, and develop a differentiable
microflake volume renderer to train the network in an end-to-end way in
principle.Our NeMF enables effective recovery of appearance attributes for
highly complex geometry and scattering object, enables high-quality relighting,
material editing, and especially simulates volume rendering effects, such as
scattering, which is infeasible for surface-based approaches.

本文提出了使用小片体积来表示场景，通过编码小片体积以进行对比表面法的体积渲染，建立了微分小片体积渲染器，以实现复杂几何和散射物体的外观属性恢复，高质量灯光、材质编辑、渲染体积散射等效果。