基于事件相机与灰度相机数据，实现精准的时空实例分割
SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop
- 融合事件流与灰度图像进行时空对齐的实例分割
- 挑战赛评估了多组方法在复杂动态场景下的分割精度
- 适合关注事件相机视觉与实时目标分割的研究者
我们介绍了与CVPR 2025事件视觉研讨会同期举办的时空实例分割（SIS）挑战赛。任务是从时空对齐的事件相机与灰度相机数据中，预测指定物体类别的像素级分割掩码。本文概述了任务定义、数据集、挑战细节及结果，并介绍排名前五团队所采用的方法。更多资源与参赛者代码详见：https://github.com/tub-rip/MouseSIS/blob/main/docs/challenge_results.md
原文摘要 · Abstract (English)
We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accurate pixel-level segmentation masks of defined object classes from spatio-temporally aligned event camera and grayscale camera data. We provide an overview of the task, dataset, challenge details and results. Furthermore, we describe the methods used by the top-5 ranking teams in the challenge. More resources and code of the participants' methods are available here: https://github.com/tub-rip/MouseSIS/blob/main/docs/challenge_results.md
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