In the context of robotic grasping, object segmentation encounters several difficulties when faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion blur, and object size variability. In response to these challenges, we propose the Graph Mixer Neural Network that includes a novel collaborative contextual mixing layer, applied to 3D event graphs formed on asynchronous events. The proposed layer is designed to spread spatiotemporal correlation within an event graph at four nearest neighbor levels parallelly. We evaluate the effectiveness of our proposed method on the Event-based Segmentation (ESD) Dataset, which includes five unique image degradation challenges, including occlusion, blur, brightness, trajectory, scale variance, and segmentation of known and unknown objects. The results show that our proposed approach outperforms state-of-the-art methods in terms of mean intersection over the union and pixel accuracy. Code available at: https://github.com/sanket0707/GNN-Mixer.git

翻译：在机器人抓取场景中，物体分割面临实时操作、遮挡、低光照、运动模糊及物体尺寸变化等动态条件的多重挑战。针对这些问题，我们提出图混合器神经网络，该网络包含一种新颖的协同上下文混合层，可应用于异步事件构建的三维事件图。所提出的层旨在并行传播事件图中四个最近邻层级的时空相关性。我们在事件驱动分割数据集上评估了所提出方法的有效性，该数据集包含五种独特的图像退化挑战，包括遮挡、模糊、亮度变化、运动轨迹、尺度变化以及已知/未知物体的分割。实验结果表明，我们的方法在平均交并比和像素精度方面均优于现有最先进方法。代码开源地址：https://github.com/sanket0707/GNN-Mixer.git