In this work, we address the problem of refining the geometry of local image
features from multiple views without known scene or camera geometry. Current
approaches to local feature detection are inherently limited in their keypoint
localization accuracy because they only operate on a single view. This
limitation has a negative impact on downstream tasks such as
Structure-from-Motion, where inaccurate keypoints lead to large errors in
triangulation and camera localization. Our proposed method naturally
complements the traditional feature extraction and matching paradigm. We first
estimate local geometric transformations between tentative matches and then
optimize the keypoint locations over multiple views jointly according to a
non-linear least squares formulation. Throughout a variety of experiments, we
show that our method consistently improves the triangulation and camera
localization performance for both hand-crafted and learned local features.

本研究提出了一种基于多视图三角化和非线性最小二乘的方法来优化局部图像特征的定位精度，实验证明该方法能够提高手工制作和学习的局部特征的三角化和相机定位性能。

局部特征几何的多视角优化

Multi-View Optimization of Local Feature Geometry

Many graphics and vision problems can be expressed as non-linear least
squares optimizations of objective functions over visual data, such as images
and meshes. The mathematical descriptions of these functions are extremely
concise, but their implementation in real code is tedious, especially when
optimized for real-time performance on modern GPUs in interactive applications.
In this work, we propose a new language, Opt (available under
this http URL), for writing these objective functions over image- or
graph-structured unknowns concisely and at a high level. Our compiler
automatically transforms these specifications into state-of-the-art GPU solvers
based on Gauss-Newton or Levenberg-Marquardt methods. Opt can generate
different variations of the solver, so users can easily explore tradeoffs in
numerical precision, matrix-free methods, and solver approaches. In our
results, we implement a variety of real-world graphics and vision applications.
Their energy functions are expressible in tens of lines of code, and produce
highly-optimized GPU solver implementations. These solver have performance
competitive with the best published hand-tuned, application-specific GPU
solvers, and orders of magnitude beyond a general-purpose auto-generated
solver.

该研究展示了一个可用于图像和视觉数据非线性最小二乘目标函数最优化的新语言 Opt，其数学描述非常简洁，但在实现过程中需要考虑到现代 GPU 在交互式应用中实时性能的优化。该语言生成的 GPU 求解器在精度、矩阵无关方法和求解器方法中均具有良好的折衷、性能优异，经实验证明，它们的性能竞争力可以与已发布的手动调优、应用特定的 GPU 求解器相媲美。