The thickness of the cortical band is linked to various neurological and
psychiatric conditions, and is often estimated through surface-based methods
such as Freesurfer in MRI studies. The DiReCT method, which calculates cortical
thickness using a diffeomorphic deformation of the gray-white matter interface
towards the pial surface, offers an alternative to surface-based methods.
Recent studies using a synthetic cortical thickness phantom have demonstrated
that the combination of DiReCT and deep-learning-based segmentation is more
sensitive to subvoxel cortical thinning than Freesurfer.
While anatomical segmentation of a T1-weighted image now takes seconds,
existing implementations of DiReCT rely on iterative image registration methods
which can take up to an hour per volume. On the other hand, learning-based
deformable image registration methods like VoxelMorph have been shown to be
faster than classical methods while improving registration accuracy. This paper
proposes CortexMorph, a new method that employs unsupervised deep learning to
directly regress the deformation field needed for DiReCT. By combining
CortexMorph with a deep-learning-based segmentation model, it is possible to
estimate region-wise thickness in seconds from a T1-weighted image, while
maintaining the ability to detect cortical atrophy. We validate this claim on
the OASIS-3 dataset and the synthetic cortical thickness phantom of Rusak et
al.

该研究提出了 CortexMorph 方法，该方法利用无监督深度学习直接回归 DiReCT 所需的变形场，结合深度学习的分割模型，可以在几秒钟内从 T1 加权图像中估计区域厚度，同时保持检测皮层萎缩的能力。

CortexMorph: 基于 VoxelMorph 的可形变配准实现快速的皮质厚度估计

CortexMorph: fast cortical thickness estimation via diffeomorphic  registration using VoxelMorph

Photometric calibration is essential to many computer vision applications.
One of its key benefits is enhancing the performance of Visual SLAM, especially
when it depends on a direct method for tracking, such as the standard KLT
algorithm. Another advantage could be in retrieving the sensor irradiance
values from measured intensities, as a pre-processing step for some vision
algorithms, such as shape-from-shading. Current photometric calibration systems
rely on a joint optimization problem and encounter an ambiguity in the
estimates, which can only be resolved using ground truth information. We
propose a novel method that solves for photometric parameters using a
sequential estimation approach. Our proposed method achieves high accuracy in
estimating all parameters; furthermore, the formulations are linear and convex,
which makes the solution fast and suitable for online applications. Experiments
on a Visual Odometry system validate the proposed method and demonstrate its
advantages.

本研究提出了一种新的序贯估计方法来解决光度参数的校准问题，该方法在 Photometric Calibration 中具有高精度，解决了现有方法的优化问题。实验证明，这种方法适用于直接法和在线应用。

OSPC：在线序列光度校准

OSPC: Online Sequential Photometric Calibration

Photometric bundle adjustment, PBA, accurately estimates geometry from video.
However, current PBA systems have a temporary map that cannot manage scene
reobservations. We present, DSM, a full monocular visual SLAM based on PBA. Its
persistent map handles reobservations, yielding the most accurate results up to
date on EuRoC for a direct method.

本研究针对现有 Photometric bundle adjustment 系统中无法处理场景重观测的问题，提出基于全单目视觉 SLAM 的 DSM 方案，实现了对场景重观测的持续跟踪和最精准估算，且在 EuRoC 研究中达到了迄今为止的最佳效果。

直接稀疏映射

Direct Sparse Mapping

Gaussian process regression generally does not scale to beyond a few
thousands data points without applying some sort of kernel approximation
method. Most approximations focus on the high eigenvalue part of the spectrum
of the kernel matrix, $K$, which leads to bad performance when the length scale
of the kernel is small. In this paper we introduce Multiresolution Kernel
Approximation (MKA), the first true broad bandwidth kernel approximation
algorithm. Important points about MKA are that it is memory efficient, and it
is a direct method, which means that it also makes it easy to approximate
$K^{-1}$ and $\mathop{\textrm{det}}(K)$.

本文介绍了一种名为 Multiresolution Kernel Approximation (MKA) 的高效内存且适用于小长度尺度的高带宽核逼近算法。