Absolute camera pose regressors estimate the position and orientation of a
camera given the captured image alone. Typically, a convolutional backbone with
a multi-layer perceptron (MLP) head is trained using images and pose labels to
embed a single reference scene at a time. Recently, this scheme was extended to
learn multiple scenes by replacing the MLP head with a set of fully connected
layers. In this work, we propose to learn multi-scene absolute camera pose
regression with Transformers, where encoders are used to aggregate activation
maps with self-attention and decoders transform latent features and scenes
encoding into pose predictions. This allows our model to focus on general
features that are informative for localization, while embedding multiple scenes
in parallel. We extend our previous MS-Transformer approach
\cite{shavit2021learning} by introducing a mixed classification-regression
architecture that improves the localization accuracy. Our method is evaluated
on commonly benchmark indoor and outdoor datasets and has been shown to exceed
both multi-scene and state-of-the-art single-scene absolute pose regressors.

使用 Transformers 学习多场景相机姿态回归，通过自注意力聚合激活图和变换潜在特征以及场景编码，提高了定位准确性。

基于 Transformer 的粗细步骤的多场景姿态回归

Coarse-to-Fine Multi-Scene Pose Regression with Transformers

Rail detection, essential for railroad anomaly detection, aims to identify
the railroad region in video frames. Although various studies on rail detection
exist, neither an open benchmark nor a high-speed network is available in the
community, making algorithm comparison and development difficult. Inspired by
the growth of lane detection, we propose a rail database and a row-based rail
detection method. In detail, we make several contributions: (i) We present a
real-world railway dataset, Rail-DB, with 7432 pairs of images and annotations.
The images are collected from different situations in lighting, road
structures, and views. The rails are labeled with polylines, and the images are
categorized into nine scenes. The Rail-DB is expected to facilitate the
improvement of rail detection algorithms. (ii) We present an efficient
row-based rail detection method, Rail-Net, containing a lightweight
convolutional backbone and an anchor classifier. Specifically, we formulate the
process of rail detection as a row-based selecting problem. This strategy
reduces the computational cost compared to alternative segmentation methods.
(iii) We evaluate the Rail-Net on Rail-DB with extensive experiments, including
cross-scene settings and network backbones ranging from ResNet to Vision
Transformers. Our method achieves promising performance in terms of both speed
and accuracy. Notably, a lightweight version could achieve 92.77% accuracy and
312 frames per second. The Rail-Net outperforms the traditional method by
50.65% and the segmentation one by 5.86%. The database and code are available
at: this https URL

本研究提出了一个包括铁路数据库和轨道行排列方法的高效铁路检测算法，通过基于行排列的选择方法，使用轻量卷积神经网络和锚定分类器实现，经实验表明，这种方式比传统方法高效且准确。

铁路检测：一种高效的基于行的网络和一个新的基准

Rail Detection: An Efficient Row-based Network and A New Benchmark

Absolute camera pose regressors estimate the position and orientation of a
camera from the captured image alone. Typically, a convolutional backbone with
a multi-layer perceptron head is trained with images and pose labels to embed a
single reference scene at a time. Recently, this scheme was extended for
learning multiple scenes by replacing the MLP head with a set of fully
connected layers. In this work, we propose to learn multi-scene absolute camera
pose regression with Transformers, where encoders are used to aggregate
activation maps with self-attention and decoders transform latent features and
scenes encoding into candidate pose predictions. This mechanism allows our
model to focus on general features that are informative for localization while
embedding multiple scenes in parallel. We evaluate our method on commonly
benchmarked indoor and outdoor datasets and show that it surpasses both
multi-scene and state-of-the-art single-scene absolute pose regressors. We make
our code publicly available from
this https URL

使用 Transformers 网络结构实现多场景相机位姿估计，相比其他模型表现更加优异。