Achieving an immersive experience enabling users to explore virtual
environments with six degrees of freedom (6DoF) is essential for various
applications such as virtual reality (VR). Wide-baseline panoramas are commonly
used in these applications to reduce network bandwidth and storage
requirements. However, synthesizing novel views from these panoramas remains a
key challenge. Although existing neural radiance field methods can produce
photorealistic views under narrow-baseline and dense image captures, they tend
to overfit the training views when dealing with \emph{wide-baseline} panoramas
due to the difficulty in learning accurate geometry from sparse $360^{\circ}$
views. To address this problem, we propose PanoGRF, Generalizable Spherical
Radiance Fields for Wide-baseline Panoramas, which construct spherical radiance
fields incorporating $360^{\circ}$ scene priors. Unlike generalizable radiance
fields trained on perspective images, PanoGRF avoids the information loss from
panorama-to-perspective conversion and directly aggregates geometry and
appearance features of 3D sample points from each panoramic view based on
spherical projection. Moreover, as some regions of the panorama are only
visible from one view while invisible from others under wide baseline settings,
PanoGRF incorporates $360^{\circ}$ monocular depth priors into spherical depth
estimation to improve the geometry features. Experimental results on multiple
panoramic datasets demonstrate that PanoGRF significantly outperforms
state-of-the-art generalizable view synthesis methods for wide-baseline
panoramas (e.g., OmniSyn) and perspective images (e.g., IBRNet, NeuRay).

本研究针对虚拟现实中宽基线全景图像合成新视角仍是一个挑战这一问题，提出了一种全景泛化球面辐射场 (PanoGRF) 方法。经过实验验证，该方法在多个全景数据集上均表现出优秀的性能，超过当前视角综合方法。

PanoGRF：适用于大基线全景的通用球面辐射场

PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline  Panoramas

We propose real-time, six degrees of freedom (6DoF), 3D face pose estimation
without face detection or landmark localization. We observe that estimating the
6DoF rigid transformation of a face is a simpler problem than facial landmark
detection, often used for 3D face alignment. In addition, 6DoF offers more
information than face bounding box labels. We leverage these observations to
make multiple contributions: (a) We describe an easily trained, efficient,
Faster R-CNN--based model which regresses 6DoF pose for all faces in the photo,
without preliminary face detection. (b) We explain how pose is converted and
kept consistent between the input photo and arbitrary crops created while
training and evaluating our model. (c) Finally, we show how face poses can
replace detection bounding box training labels. Tests on AFLW2000-3D and BIWI
show that our method runs at real-time and outperforms state of the art (SotA)
face pose estimators. Remarkably, our method also surpasses SotA models of
comparable complexity on the WIDER FACE detection benchmark, despite not been
optimized on bounding box labels.

本文提出了一种实时的、无需面部检测或关键点定位的六自由度、三维人脸姿态估计方法，使用了 Faster R-CNN 模型将六自由度位姿回归给图像中所有的脸部，并展示了该方法在 AFLW2000-3D 和 BIWI 数据集上的优越性能。

img2pose: 基于 6DoF 的人脸姿态估计，面部对齐和检测

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

We propose an approach to estimate the 6DOF pose of a satellite, relative to
a canonical pose, from a single image. Such a problem is crucial in many space
proximity operations, such as docking, debris removal, and inter-spacecraft
communications. Our approach combines machine learning and geometric
optimisation, by predicting the coordinates of a set of landmarks in the input
image, associating the landmarks to their corresponding 3D points on an a
priori reconstructed 3D model, then solving for the object pose using
non-linear optimisation. Our approach is not only novel for this specific pose
estimation task, which helps to further open up a relatively new domain for
machine learning and computer vision, but it also demonstrates superior
accuracy and won the first place in the recent Kelvins Pose Estimation
Challenge organised by the European Space Agency (ESA).

我们提出了一种从单个图像中估计卫星 6DOF 姿态的方法，该方法结合了机器学习和几何优化，并预测了输入图像中一组地标的坐标，解决了非线性优化以求解物体姿态，不仅在该特定姿态估计任务中具有新颖性，还在欧洲航天局（ESA）组织的 Kelvins 姿态估计挑战赛中展现出优秀的精确度，获得了第一名。