AI Insight
This paper introduces Active Spiking Perception (ASP), a novel approach to 3D point cloud recognition that uses spiking neural networks to actively select which portions of a scene to observe next, based on the network's evolving membrane potential interpreted as a running belief about object class. The system achieves competitive accuracy on standard benchmarks (90.62% on ModelNet40, 93.28% on ModelNet10) while enabling early exit decisions that reduce computational costs by 1.35x to 2.8x compared to processing the entire point cloud. The authors provide theoretical guarantees showing that the leaky integration mechanism implements Bayesian filtering and that the stopping rule maintains statistical validity without multiple-testing penalties.
Why it matters
This work demonstrates a practical method for reducing energy consumption in 3D recognition systems by up to 74%, which could enable more efficient deployment of computer vision in resource-constrained environments like autonomous vehicles, robotics, and mobile devices. The anytime decision framework allows systems to trade accuracy for speed based on available computational budget.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distinctive resource of spiking computation, the temporal evolution of the membrane potential, unused as a locus of decision-making. Active Spiking Perception (ASP) recasts 3D recognition as an iterative decision process in which the network’s own leaky integrate-and-fire (LIF) membrane potential, read as a running belief over the class, selects the next chunk to observe and triggers confidence-margin early exit. A lightweight Slice-Selection Policy scores unvisited farthest-point-sampled chunks from the membrane state and precomputed geometric descriptors, trains end-to-end through a straight-through Gumbel-Softmax, reduces to an argmax at inference, and adds about 2% of backbone parameters. We prove that leaky integration is the recursive log-posterior update of a Bayesian filter, that the exit rule attains distribution-free selective risk with no multiple-testing penalty at the stopping time, and that streaming state carry-forward is exactly equivalent to prefix recomputation with bounded finite-precision drift. ASP reaches 90.62% and 93.28% on ModelNet40 and ModelNet10, 1.7 points below the strongest spiking baseline at a larger backbone, while adding a certified anytime interface no baseline offers. The mechanism transfers unchanged to dense prediction, giving 83.21 instance mIoU on ShapeNetPart and 48.50 mIoU on S3DIS Area 5, to our knowledge the first spiking results on S3DIS Area 5, and, fixation replacing chunk selection, to a foveated non-spiking transformer, so the policy is not tied to spiking backbones: cost is exactly linear in observations and the threshold is a measured compute dial spanning 2.8x to 1.35x less energy. One limitation is concrete: one S3DIS class is unidentifiable at the crop size we use, and we give the prediction that would fix it.