提出Labits表示法，提升事件相机轨迹估计精度
Labits: Layered Bidirectional Time Surfaces Representation for Event Camera-based Continuous Dense Trajectory Estimation
- 分层双向时间表面设计，保留精细时间与视觉特征
- 在MultiFlow数据集上轨迹终点误差降低49%
- 适合事件相机、实时运动追踪研究者
事件相机以高时间分辨率和低延迟捕捉动态场景，运动物体触发带有精确时间戳的事件，支持连续时间估计。然而，现有方法在事件表示构建中信息损失严重，制约了性能上限。高效利用事件相机需同时保留细粒度时间信息、稳定且具表征力的2D视觉特征，以及时间上一致的信息密度，这在现有表示中尚未实现。本文提出Labits：分层双向时间表面，一种简洁而优雅的表示方法，可完整保留上述特性。此外，设计专用模块提取活动像素局部光流（APLOF），显著提升性能。在MultiFlow数据集上，本方法相比先前最优方法，轨迹终点误差（TEPE）降低49%。代码将在论文接受后发布。
原文摘要 · Abstract (English)
Event cameras provide a compelling alternative to traditional frame-based sensors, capturing dynamic scenes with high temporal resolution and low latency. Moving objects trigger events with precise timestamps along their trajectory, enabling smooth continuous-time estimation. However, few works have attempted to optimize the information loss during event representation construction, imposing a ceiling on this task. Fully exploiting event cameras requires representations that simultaneously preserve fine-grained temporal information, stable and characteristic 2D visual features, and temporally consistent information density, an unmet challenge in existing representations. We introduce Labits: Layered Bidirectional Time Surfaces, a simple yet elegant representation designed to retain all these features. Additionally, we propose a dedicated module for extracting active pixel local optical flow (APLOF), significantly boosting the performance. Our approach achieves an impressive 49% reduction in trajectory end-point error (TEPE) compared to the previous state-of-the-art on the MultiFlow dataset. The code will be released upon acceptance.
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