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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.16414 |
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| _version_ | 1866913133666238464 |
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| author | Mansour, Omar Martinello, Pietro Milon, Ethan Xu, YingFu Sifalakis, Manolis Tang, Guangzhi Yousefzadeh, Amirreza |
| author_facet | Mansour, Omar Martinello, Pietro Milon, Ethan Xu, YingFu Sifalakis, Manolis Tang, Guangzhi Yousefzadeh, Amirreza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16414 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research Mansour, Omar Martinello, Pietro Milon, Ethan Xu, YingFu Sifalakis, Manolis Tang, Guangzhi Yousefzadeh, Amirreza Computer Vision and Pattern Recognition 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. |
| title | NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.16414 |