基于稀疏点云导出的深度事件视觉里程计扩展
Extending Deep Event Visual Odometry with Sparse Point-Cloud Export
AI总结：
通过添加稀疏点云导出管线，将DEVO内部估计的3D结构转化为显式点云表示，实现可视化与后续处理，在BOARD SLOW序列上验证了局部一致性。
摘要：
事件相机因其低延迟、高时间分辨率和高动态范围，非常适合高速运动和挑战性光照条件下的视觉里程计。深度事件视觉里程计（DEVO）通过结合稀疏块跟踪、学习块选择、循环对应精化和可微束调整，证明了单目纯事件里程计能够实现强性能。在本项目中，我们通过稀疏点云导出管线扩展了DEVO。我们的方法不修改核心里程计公式，而是暴露DEVO已估计的内部3D结构，并将其转换为显式点云表示，用于可视化和进一步处理。此外，我们实现了一个实用的工作流程，用于数据导出、格式转换和点云清理。最终系统保留了原始视觉里程计管线，同时支持稀疏几何场景输出。在BOARD SLOW序列上的实验表明，导出的稀疏点云与EMVS重建在局部一致，在5厘米阈值下达到高精度，同时也突出了在密度、完整性和对累积里程计噪声敏感性方面的预期局限性。
英文摘要：
Event cameras are well suited for visual odometry under high-speed motion and challenging lighting conditions due to their low latency, high temporal resolution, and high dynamic range. Deep Event Visual Odometry (DEVO) demonstrated that monocular event-only odometry can achieve strong performance by combining sparse patch tracking, learned patch selection, recurrent correspondence refinement, and differentiable bundle adjustment. In this project, we extend DEVO with a sparse point-cloud export pipeline. Rather than modifying the core odometry formulation, our approach exposes the internal 3D structure already estimated by DEVO and converts it into an explicit point-cloud representation for visualization and further processing. In addition, we implement a practical workflow for data export, format conversion, and point-cloud cleanup. The resulting system preserves the original visual odometry pipeline while enabling sparse geometric scene output. Experiments on the BOARD SLOW sequence show that the exported sparse cloud is locally consistent with EMVS reconstructions, achieving high precision at a 5 cm threshold, while also highlighting the expected limitations in density, completeness, and sensitivity to accumulated odometry noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
