# Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform

arXiv: https://arxiv.org/abs/2203.15439 · HTML: https://mlsanity.com/paper/2203.15439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Semi-Dense 3D Reconstruction with a Stereo Event Camera](https://mlsanity.com/paper/1807.07429/) (2018)
- [eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform](https://mlsanity.com/paper/1906.05096/) (2019)
- [A Scalable Pipelined Dataflow Accelerator for Object Region Proposals on FPGA Platform](https://mlsanity.com/paper/1810.12137/) (2018)

## Built on by (most cited first, 3 of 3)

- [Event-based vision on FPGAs -- a survey](https://mlsanity.com/paper/2407.08356/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices](https://mlsanity.com/paper/2408.12840/) (2024)

## Abstract (arXiv)

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× improvement in energy efficiency compared with Intel i5 CPU platform.