Abstract
Event cameras produce asynchronous, high-dynamic-range streams well suited for detecting small, fast-moving drones, yet most event-based detectors convert the sparse event stream into dense tensors, discarding the representational efficiency of neuromorphic sensing. We propose SparseVoxelDet, to our knowledge the first fully sparse object detector for event cameras, in which backbone feature extraction, feature pyramid fusion, and the detection head all operate exclusively on occupied voxel positions through 3D sparse convolutions; no dense feature tensor is instantiated at any stage of the pipeline. On the FRED benchmark (629,832 annotated frames), SparseVoxelDet achieves 83.38% mAP at 50 while processing only 14,900 active voxels per frame (0.23% of the T.H.W grid), compared to 409,600 pixels for the dense YOLOv11 baseline (87.68% mAP at 50). Relaxing the IoU threshold from 0.50 to 0.40 recovers mAP to 89.26%, indicating that the remaining accuracy gap is dominated by box regression precision rather than detection capability. The sparse representation yields 858 times GPU memory compression and 3,670 times storage reduction relative to the equivalent dense 3D voxel tensor, with data-structure size that scales with scene dynamics rather than sensor resolution. Error forensics across 119,459 test frames confirms that 71 percent of failures are localization near-misses rather than missed targets. These results demonstrate that native sparse processing is a viable paradigm for event-camera object detection, exploiting the structural sparsity of neuromorphic sensor data without requiring neuromorphic computing hardware, and providing a framework whose representation cost is governed by scene activity rather than pixel count, a property that becomes increasingly valuable as event cameras scale to higher resolutions.
Abstract (translated)
事件相机产生的异步高动态范围流非常适用于检测小型快速移动的无人机，但大多数基于事件的检测器会将稀疏事件流转换为稠密张量，从而丢弃了神经形态传感的表征效率。我们提出了SparseVoxelDet，据我们所知，这是首个面向事件相机的全稀疏目标检测器，其骨干特征提取、特征金字塔融合和检测头均通过3D稀疏卷积仅在占据体素位置上进行运算；管道中任何阶段都不会实例化稠密特征张量。在FRED基准测试（629,832个标注帧）上，SparseVoxelDet在每帧仅处理14,900个活跃体素（占T.H.W网格的0.23%）的情况下，实现了50%交并比下83.38%的平均精度，而稠密YOLOv11基线（50%交并比下87.68%的平均精度）需处理409,600个像素。将交并比阈值从0.50放宽至0.40后，平均精度恢复至89.26%，表明剩余的精度差距主要源于边界框回归精度而非检测能力。稀疏表征相较于等效的稠密3D体素张量，实现了GPU内存压缩858倍与存储缩减3,670倍，且数据结构尺寸随场景动态变化而非传感器分辨率增长。对119,459个测试帧的错误溯源分析证实，71%的失败案例源于定位偏差而非漏检目标。这些结果表明，原生稀疏处理是事件相机目标检测的可行范式，它无需神经形态计算硬件即可利用神经形态传感器数据的结构稀疏性，并提供了一种表示成本由场景活动度而非像素数量决定的框架——这一特性在事件相机向更高分辨率发展时将愈发重要。
URL
https://arxiv.org/abs/2603.21638
