SparseVoxelDet: Fully Sparse Voxel Networks for Efficient Event-Based Drone Detection

Mohamad Yazan Sadoun; Sarah Sharif; Yaser Mike Banad

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arXiv · 2603.21638

SparseVoxelDet: Fully Sparse Voxel Networks for Efficient Event-Based Drone Detection

Mohamad Yazan Sadoun·Sarah Sharif·Yaser Mike Banad

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Abstract

Event cameras excel at detecting small, fast drones, but today's detectors give away their key advantage: they convert the sparse event stream into dense grids and pay dense-processing cost on inputs that are almost entirely empty. We present SparseVoxelDet, to our knowledge the first coordinate-sparse 3D event voxel bounding-box detector: backbone, feature pyramid, temporal reduction, and detection head all operate directly on coordinate-indexed features, with no dense spatial grid at any stage. Building it exposed a hidden failure mode we name support inflation: an input filling a median 0.0652% of the voxel lattice inflates stage by stage until standard pyramid fusion leaves the detection head locally near-dense. We answer with two ideas. Expansion-free inverse-convolution fusion provably creates no active sites beyond the stored backbone supports, cutting head occupancy from a median 78.88% to 10.53%; quality-aligned supervision then recovers more accuracy than preserving sparsity costs. The payoff is measured, not assumed: executing the same trained network densely costs a median 27.5x the work and 4.65x the latency across 5,000 paired frames, with no frame cheaper dense, while the 6.22M-parameter detector reaches 87.01 AP50 on the FRED drone benchmark, ahead of matched dense controls, and holds its lead on the held-out test partition evaluated once. Sparsity, preserved by construction and supervised well, delivers the efficiency and the accuracy together.

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Mohamad Yazan Sadoun, Sarah Sharif, Yaser Mike Banad. 2026-03-23. SparseVoxelDet: Fully Sparse Voxel Networks for Efficient Event-Based Drone Detection. https://arxiv.org/abs/2603.21638

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