Event6D：基于事件相机的新物体 6D 位姿跟踪
Event6D: Event-based Novel Object 6D Pose Tracking
用事件流在深度帧之间重建稠密强度与深度，为高速运动下的 6D 位姿跟踪提供几何与光度线索；其「以最近深度为条件、双重建事件」的思路可迁移到事件相机驱动的 SLAM、抓取位姿跟踪与高速场景重建。
原文依据 reconstructing both intensity and depth at arbitrary timestamps between depth frames
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论事件相机具备微秒级延迟，因而适用于快速、动态场景下的 6D 物体位姿跟踪；在这些场景中，传统 RGB 与深度流水线会受运动模糊和大像素位移的影响。我们提出 EventTrack6D，一种事件-深度跟踪框架，通过在深度帧之间的任意时间戳重建强度与深度，无需针对特定物体训练即可泛化到新物体。以最近的深度测量为条件，我们的双重重建从稀疏事件流中恢复稠密的光度与几何线索。EventTrack6D 运行速度超过 120 FPS，并在快速运动下保持时序一致性。为支持训练与评测，我们提出了一套完整的基准套件：一个用于训练的大规模合成数据集，以及两个互补的评测集，包括真实与仿真事件数据集。EventTrack6D 仅在合成数据上训练，无需微调即可有效泛化到真实场景，在多样物体与运动模式下保持准确跟踪。我们的方法与数据集验证了事件相机用于新物体事件式 6D 位姿跟踪的有效性。代码与数据集已公开于 https://chohoonhee.github.io/Event6D。
英文原文摘要 1281 字符
Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion blur and large pixel displacements. We introduce EventTrack6D, an event-depth tracking framework that generalizes to novel objects without object-specific training by reconstructing both intensity and depth at arbitrary timestamps between depth frames. Conditioned on the most recent depth measurement, our dual reconstruction recovers dense photometric and geometric cues from sparse event streams. Our EventTrack6D operates at over 120 FPS and maintains temporal consistency under rapid motion. To support training and evaluation, we introduce a comprehensive benchmark suite: a large-scale synthetic dataset for training and two complementary evaluation sets, including real and simulated event datasets. Trained exclusively on synthetic data, EventTrack6D generalizes effectively to real-world scenarios without fine-tuning, maintaining accurate tracking across diverse objects and motion patterns. Our method and datasets validate the effectiveness of event cameras for event-based 6D pose tracking of novel objects. Code and datasets are publicly available at https://chohoonhee.github.io/Event6D.