用事件相机直接优化亮度图，实现高精度姿态与场景重建。
Event-based Photometric Bundle Adjustment
- 基于事件生成模型构建光度误差，直接优化旋转与亮度图。
- 在真实数据上使光度误差降低90%，细节还原更清晰。
- 适用于全景成像，无需初始化，兼容惯性/先前估计方法。
针对纯旋转事件相机的捆绑调整问题，本文从基础原理出发，将问题建模为经典的非线性最小二乘优化。通过直接利用事件生成模型定义光度误差，结合相机旋转与半稠密场景亮度图来触发事件。利用事件数据的稀疏性，设计出可处理大规模变量的Levenberg-Marquardt求解器。据我们所知，本方法——事件光度捆绑调整（EPBA）——是首个直接对亮度图进行优化、充分利用事件时空特性的纯事件相机光度捆绑调整方法，无需将事件转换为图像形式。在合成与真实数据集上的综合实验表明，EPBA可将光度误差降低高达90%，结果质量显著优于现有仅估计旋转的方法。优化后的场景地图揭示了此前方法无法捕捉的细节。在现代高分辨率事件相机上的实验验证了其在多种场景下的适用性：无需地图初始化、支持多分辨率、可与IMU死推算或先前事件相机旋转估计方法结合使用。源代码已公开。https://github.com/tub-rip/epba
原文摘要 · Abstract (English)
We tackle the problem of bundle adjustment (i.e., simultaneous refinement of camera poses and scene map) for a purely rotating event camera. Starting from first principles, we formulate the problem as a classical non-linear least squares optimization. The photometric error is defined using the event generation model directly in the camera rotations and the semi-dense scene brightness that triggers the events. We leverage the sparsity of event data to design a tractable Levenberg-Marquardt solver that handles the very large number of variables involved. To the best of our knowledge, our method, which we call Event-based Photometric Bundle Adjustment (EPBA), is the first event-only photometric bundle adjustment method that works on the brightness map directly and exploits the space-time characteristics of event data, without having to convert events into image-like representations. Comprehensive experiments on both synthetic and real-world datasets demonstrate EPBA's effectiveness in decreasing the photometric error (by up to 90%), yielding results of unparalleled quality. The refined maps reveal details that were hidden using prior state-of-the-art rotation-only estimation methods. The experiments on modern high-resolution event cameras show the applicability of EPBA to panoramic imaging in various scenarios (without map initialization, at multiple resolutions, and in combination with other methods, such as IMU dead reckoning or previous event-based rotation estimation methods). We make the source code publicly available. https://github.com/tub-rip/epba
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