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About this paper
Aayush Atul Verma, Arpitsinh Vaghela, Bharatesh Chakravarthi, Kaustav Chanda, Yezhou Yang
The eGSMV framework from Arizona State University processes raw event camera data as a spatio-temporal multigraph for object detection, using decoupled spatial and motion vector learning to maintain asynchronous processing. This approach achieves higher detection accuracy and computational efficiency compared to prior graph-based methods on the Gen1 and eTraM datasets.
Key insights
- Explicitly modeling both spatial structure and temporal motion using decoupled graph edges and specialized kernels is crucial for effective event-based object detection.
- Utilizing anisotropic 2D spline convolution kernels for spatial feature learning significantly reduces computational complexity and model parameters while maintaining high detection performance.
- Maintaining event-level granularity in the detection head, rather than densifying or voxelizing, is essential for maximizing detection accuracy and preserving asynchronous processing benefits.
Summary by alphaXiv.
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