In this work, we aim to detect the changes caused by object variations in a
scene represented by the neural radiance fields (NeRFs). Given an arbitrary
view and two sets of scene images captured at different timestamps, we can
predict the scene changes in that view, which has significant potential
applications in scene monitoring and measuring. We conducted preliminary
studies and found that such an exciting task cannot be easily achieved by
utilizing existing NeRFs and 2D change detection methods with many false or
missing detections. The main reason is that the 2D change detection is based on
the pixel appearance difference between spatial-aligned image pairs and
neglects the stereo information in the NeRF. To address the limitations, we
propose the C-NERF to represent scene changes as directional consistency
difference-based NeRF, which mainly contains three modules. We first perform
the spatial alignment of two NeRFs captured before and after changes. Then, we
identify the change points based on the direction-consistent constraint; that
is, real change points have similar change representations across view
directions, but fake change points do not. Finally, we design the change map
rendering process based on the built NeRFs and can generate the change map of
an arbitrarily specified view direction. To validate the effectiveness, we
build a new dataset containing ten scenes covering diverse scenarios with
different changing objects. Our approach surpasses state-of-the-art 2D change
detection and NeRF-based methods by a significant margin.

该研究论文提出了一种基于神经辐射场 (NeRF) 的方向一致性差异的场景变化检测方法，通过对两个时间戳拍摄的场景图像进行空间对齐和方向一致性约束，能够有效地生成指定视角的变化地图。实验证明，该方法在 2D 变化检测和基于 NeRF 的方法方面显著超越了现有最先进的方法。

C-NERF: 基于方向一致性差异的场景变化表示

C-NERF: Representing Scene Changes as Directional Consistency  Difference-based NeRF

We present CPO, a fast and robust algorithm that localizes a 2D panorama with
respect to a 3D point cloud of a scene possibly containing changes. To robustly
handle scene changes, our approach deviates from conventional feature point
matching, and focuses on the spatial context provided from panorama images.
Specifically, we propose efficient color histogram generation and subsequent
robust localization using score maps. By utilizing the unique equivariance of
spherical projections, we propose very fast color histogram generation for a
large number of camera poses without explicitly rendering images for all
candidate poses. We accumulate the regional consistency of the panorama and
point cloud as 2D/3D score maps, and use them to weigh the input color values
to further increase robustness. The weighted color distribution quickly finds
good initial poses and achieves stable convergence for gradient-based
optimization. CPO is lightweight and achieves effective localization in all
tested scenarios, showing stable performance despite scene changes, repetitive
structures, or featureless regions, which are typical challenges for visual
localization with perspective cameras.

CPO 是一种针对 2D 全景图像和 3D 点云进行快速和鲁棒定位的算法，通过使用色彩直方图和得分图进行可靠定位，其重点是处理包含场景变化的情况。

CPO: 改进鲁棒性普通全景转换为点云定位

CPO: Change Robust Panorama to Point Cloud Localization

Long-term visual localization is the problem of estimating the camera pose of
a given query image in a scene whose appearance changes over time. It is an
important problem in practice, for example, encountered in autonomous driving.
In order to gain robustness to such changes, long-term localization approaches
often use segmantic segmentations as an invariant scene representation, as the
semantic meaning of each scene part should not be affected by seasonal and
other changes. However, these representations are typically not very
discriminative due to the limited number of available classes. In this paper,
we propose a new neural network, the Fine-Grained Segmentation Network (FGSN),
that can be used to provide image segmentations with a larger number of labels
and can be trained in a self-supervised fashion. In addition, we show how FGSNs
can be trained to output consistent labels across seasonal changes. We
demonstrate through extensive experiments that integrating the fine-grained
segmentations produced by our FGSNs into existing localization algorithms leads
to substantial improvements in localization performance.

本文研究长期视觉定位问题，提出了 Fine-Grained Segmentation Network（FGSN）用于提供更多标签的图像分割，并能自我学习以应对季节性变化，实验证明将 FGSN 的细粒度分割整合到现有的定位算法中可显著提高定位精度。

细粒度分割网络：自监督分割以提高长期视觉定位

Fine-Grained Segmentation Networks: Self-Supervised Segmentation for  Improved Long-Term Visual Localization

In this paper, we propose a novel model with a hierarchical photo-scene
encoder and a reconstructor for the task of album storytelling. The photo-scene
encoder contains two sub-encoders, namely the photo and scene encoders, which
are stacked together and behave hierarchically to fully exploit the structure
information of the photos within an album. Specifically, the photo encoder
generates semantic representation for each photo while exploiting temporal
relationships among them. The scene encoder, relying on the obtained photo
representations, is responsible for detecting the scene changes and generating
scene representations. Subsequently, the decoder dynamically and attentively
summarizes the encoded photo and scene representations to generate a sequence
of album representations, based on which a story consisting of multiple
coherent sentences is generated. In order to fully extract the useful semantic
information from an album, a reconstructor is employed to reproduce the
summarized album representations based on the hidden states of the decoder. The
proposed model can be trained in an end-to-end manner, which results in an
improved performance over the state-of-the-arts on the public visual
storytelling (VIST) dataset. Ablation studies further demonstrate the
effectiveness of the proposed hierarchical photo-scene encoder and
reconstructor.

本文提出了一种新模型，该模型包含具有层次结构的照片场景编码器和再构造器，用于相册故事叙述的任务。该模型以端到端的方式训练，在公共视觉叙事（VIST）数据集上取得了优异的性能。