Event cameras are a paradigm shift in camera technology. Instead of full
frames, the sensor captures a sparse set of events caused by intensity changes.
Since only the changes are transferred, those cameras are able to capture quick
movements of objects in the scene or of the camera itself. In this work we
propose a novel method to perform camera tracking of event cameras in a
panoramic setting with three degrees of freedom. We propose a direct camera
tracking formulation, similar to state-of-the-art in visual odometry. We show
that the minimal information needed for simultaneous tracking and mapping is
the spatial position of events, without using the appearance of the imaged
scene point. We verify the robustness to fast camera movements and dynamic
objects in the scene on a recently proposed dataset and self-recorded
sequences.

本文提出了一种新的方法，用于在具有三个自由度的全景设置中进行事件摄像机的摄像机跟踪。该方法利用事件的空间位置而不使用图像场景点的外观进行跟踪。结果表明该方法能够应对快速移动的物体和场景中的动态物体。

事件相机实时全景跟踪

Real-Time Panoramic Tracking for Event Cameras

We target the problem of sparse 3D reconstruction of dynamic objects observed
by multiple unsynchronized video cameras with unknown temporal overlap. To this
end, we develop a framework to recover the unknown structure without sequencing
information across video sequences. Our proposed compressed sensing framework
poses the estimation of 3D structure as the problem of dictionary learning,
where the dictionary is defined as an aggregation of the temporally varying 3D
structures. Given the smooth motion of dynamic objects, we observe any element
in the dictionary can be well approximated by a sparse linear combination of
other elements in the same dictionary (i. e. self-expression). Moreover, the
sparse coefficients describing a locally linear 3D structural interpolation
reveal the local sequencing information. Our formulation optimizes a biconvex
cost function that leverages a compressed sensing formulation and enforces both
structural dependency coherence across video streams, as well as motion
smoothness across estimates from common video sources. We further analyze the
reconstructability of our approach under different capture scenarios, and its
comparison and relation to existing methods. Experimental results on large
amounts of synthetic data as well as real imagery demonstrate the effectiveness
of our approach.

本文提出了一种基于压缩感知技术和字典学习的三维重建方法，可以恢复无序视频序列中动态物体的时变结构，并且在多个视频流中具有结构依赖性和运动平滑性，实验结果表明了该方法的有效性。