Indoor relocalization is vital for both robotic tasks like autonomous
exploration and civil applications such as navigation with a cell phone in a
shopping mall. Some previous approaches adopt geometrical information such as
key-point features or local textures to carry out indoor relocalization, but
they either easily fail in an environment with visually similar scenes or
require many database images. Inspired by the fact that humans often remember
places by recognizing unique landmarks, we resort to objects, which are more
informative than geometry elements. In this work, we propose a simple yet
effective object-based indoor relocalization approach, dubbed AirLoc. To
overcome the critical challenges of object reidentification and remembering
object relationships, we extract object-wise appearance embedding and
inter-object geometric relationships. The geometry and appearance features are
integrated to generate cumulative scene features. This results in a robust,
accurate, and portable indoor relocalization system, which outperforms the
state-of-the-art methods in room-level relocalization by 9.5% of PR-AUC and 7%
of accuracy. In addition to exhaustive evaluation, we also carry out real-world
tests, where AirLoc shows robustness in challenges like severe occlusion,
perceptual aliasing, viewpoint shift, and deformation.

本文提出了一种简单而有效的基于物体的室内重定位方法，称为 AirLoc。通过提取物体外观嵌入和物体间的几何关系来解决物体重新识别和记忆物体关系的关键挑战，并将几何和外观特征相结合生成累积场景特征，从而实现了强大、准确、便携的室内重定位系统，并在室内重定位的 PR-AUC 和准确性方面优于现有方法 9.5% 和 7%。

AirLoc：基于目标的室内重定位

AirLoc: Object-based Indoor Relocalization

Neural Radiance Fields (NeRF) has recently gained popularity for its
impressive novel view synthesis ability. This paper studies the problem of
hallucinated NeRF: i.e., recovering a realistic NeRF at a different time of day
from a group of tourism images. Existing solutions adopt NeRF with a
controllable appearance embedding to render novel views under various
conditions, but they cannot render view-consistent images with an unseen
appearance. To solve this problem, we present an end-to-end framework for
constructing a hallucinated NeRF, dubbed as Ha-NeRF. Specifically, we propose
an appearance hallucination module to handle time-varying appearances and
transfer them to novel views. Considering the complex occlusions of tourism
images, we introduce an anti-occlusion module to decompose the static subjects
for visibility accurately. Experimental results on synthetic data and real
tourism photo collections demonstrate that our method can hallucinate the
desired appearances and render occlusion-free images from different views. The
project and supplementary materials are available at
this https URL

研究 Hallucinated Neural Radiance Fields（Ha-NeRF）的问题，提出一种端到端框架，包括外观幻化模块和反遮挡模块，能够处理旅游照片中存在的外观变化和复杂遮挡，从不同视角生成无遮挡的一致性视图。

野外虚构神经颜色空间

Hallucinated Neural Radiance Fields in the Wild

Online tracking of multiple objects in videos requires strong capacity of
modeling and matching object appearances. Previous methods for learning
appearance embedding mostly rely on instance-level matching without considering
the temporal continuity provided by videos. We design a new instance-to-track
matching objective to learn appearance embedding that compares a candidate
detection to the embedding of the tracks persisted in the tracker. It enables
us to learn not only from videos labeled with complete tracks, but also
unlabeled or partially labeled videos. We implement this learning objective in
a unified form following the spirit of constrastive loss. Experiments on
multiple object tracking datasets demonstrate that our method can effectively
learning discriminative appearance embeddings in a semi-supervised fashion and
outperform state of the art methods on representative benchmarks.

本文提出了一种基于实例到轨迹匹配的外观嵌入学习方法，可以处理在线多目标跟踪问题，该方法可以进行半监督学习，并且在多个数据集上都表现出了更好的性能。