  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
