# ECCV'26 open-sources MV2GF: using a visual geometry foundation model to improve cross-view pedestrian detection generalization, with MODA up by as much as 4.7 points

- Source: 3D视觉工坊 · 微信公众号
- Published: 2026-10-26T23:00:00.000Z
- RoboHot: https://robohot.news/en/items/p2exb05bx6ixrsfabc2bmypvl
- Original: https://mp.weixin.qq.com/s?__biz=MzU1MjY4MTA1MQ==&mid=2247752609&idx=2&sn=0d360bc086221b2d36ff82254b91b7f0&chksm=fa8a0196aa4a2f83d0ad2c2e2cfe5f26f0e300d6a67555c26b5806d4ca8be80f821ead4eca1c&scene=126&sessionid=1793095228#rd

## Summary

An ECCV'26 paper proposes MV2GF, a multi-view pedestrian detection method that uses the multi-view features and 3D point maps of the pretrained visual geometry foundation model DA3 to aggregate BEV features instead of perspective projection. Under the setting where training and test camera configurations differ, MV2GF achieves MODA on GMVD-D, MVPerception, and Wildtrack that is 4.6, 4.7, and 2.2 points higher than the previous best, respectively.