# NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research

Source: [NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research](<https://www.sota2.com/research/paper/6a0bee87dbc836b87d82c370>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-20T05:00:05.687Z

Authors: Omar Mansour, Pietro Martinello, Ethan Milon, YingFu Xu, Manolis Sifalakis, Guangzhi Tang, Amirreza Yousefzadeh

Published: 2026-05-13T16:35:27.000Z

## Abstract

We present NERVE (Neuromorphic Vision and Radar Ensemble), a multi-sensor dataset comprising 257 minutes of synchronized recordings from five sensors: two Dynamic Vision Sensors (DVS), an RGB-D camera, and two Radar units (24GHz and 77GHz). Captured across 12 measurement days in office environments, NERVE contains around 600GB of uncompressed temporally aligned data with around 914,000 frames and around 9.6 million RGB COCO-formatted annotations covering 16 relevant object categories. To evaluate multi-modal fusion, we construct a DVS+Radar subset for human detection and distance estimation. Baseline experiments using feed-forward and recurrent detectors show that combining DVS with 77GHz Radar consistently improves detection, with recurrent models achieving up to 47.5% mAP and mean absolute Radar distance errors below 1.8m against LiDAR ground truth.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.16414>)

[PDF](<https://arxiv.org/pdf/2605.16414.pdf>)