Event cameras are bio-inspired vision sensors that asynchronously represent pixel-level brightness changes as event streams. Event-based monocular multi-view stereo (EMVS) is a technique that exploits the event streams to estimate semi-dense 3D structure with known trajectory. It is a critical task for event-based monocular SLAM. However, the required intensive computation workloads make it challenging for real-time deployment on embedded platforms. In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA. Highly paralleled and fully pipelined processing elements are specially designed via FPGA and integrated with the embedded ARM as a heterogeneous system to improve the throughput and reduce the memory footprint. Meanwhile, the EMVS algorithm is reformulated to a more hardware-friendly manner by rescheduling, approximate computing and hybrid data quantization. Evaluation results on DAVIS dataset show that Eventor achieves up to $24\times$ improvement in energy efficiency compared with Intel i5 CPU platform.
翻译:活动相机是生物激励式的视觉传感器,它作为事件流,不同步地代表像素水平的亮度变化。事件单向多视立体(EMVS)是一种技术,利用事件流来估计已知轨迹的半热三维结构。这是事件单向SLAM的一项关键任务。然而,由于所需的密集计算工作量,在嵌入平台上实时部署是困难的。在本文中,活动器被提议为快速和高效的 EMVS加速器,实现最关键和最耗时的阶段,包括在FPGA上的事件后投射和量射线计。高度平行和完全编程的处理元件是通过FPGA专门设计的,并与嵌入式的ARM整合为混合系统,以改善吞吐量和减少记忆足迹。与此同时,EMVS算法通过重新安排时间、估计计算和混合数据量化。DVIS数据集的评价结果显示,与Intel i5 CPU平台相比,活动器能效提高了24美元。