用事件相机实现高精度全景拼接，无需图像转换
Event-based Mosaicing Bundle Adjustment
- 基于事件生成模型与全景梯度图构建优化目标
- 50%降低光度误差，实现前所未有的拼接质量
- 首次利用稀疏结构加速事件相机优化，适合高分辨率场景
针对纯旋转事件相机的全景拼接束调整问题，本文提出一种正则化的非线性最小二乘优化方法。目标函数基于相机姿态的线性化事件生成模型与场景的全景梯度图定义。我们证明该束调整问题具有可利用的块对角稀疏结构，从而实现高效求解。据我们所知，这是首个在不将事件转换为图像表示的前提下，利用此类稀疏性加速事件相机优化的工作。我们在合成与真实数据集上评估了名为EMBA的方法，验证其有效性（光度误差降低50%），并成功应用于高分辨率事件相机，在无初始地图情况下生成精细全景图。项目页面：https://github.com/tub-rip/emba
原文摘要 · Abstract (English)
We tackle the problem of mosaicing bundle adjustment (i.e., simultaneous refinement of camera orientations and scene map) for a purely rotating event camera. We formulate the problem as a regularized non-linear least squares optimization. The objective function is defined using the linearized event generation model in the camera orientations and the panoramic gradient map of the scene. We show that this BA optimization has an exploitable block-diagonal sparsity structure, so that the problem can be solved efficiently. To the best of our knowledge, this is the first work to leverage such sparsity to speed up the optimization in the context of event-based cameras, without the need to convert events into image-like representations. We evaluate our method, called EMBA, on both synthetic and real-world datasets to show its effectiveness (50% photometric error decrease), yielding results of unprecedented quality. In addition, we demonstrate EMBA using high spatial resolution event cameras, yielding delicate panoramas in the wild, even without an initial map. Project page: https://github.com/tub-rip/emba
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