very interesting work, the output is very clean how does it deal with lens distortion? is it compensated by the FBA where the sample with the least distortion gets chosen as the reference? or does the FBA internally have some estimation of the distortion and applies that to all keypoints from the same sample?
![[ECCV 2024] GeoCalib: Learning Single-image Calibration with Geometric Optimization](http://i.ytimg.com/vi/uOmTwvKreM4/mqdefault.jpg)
![[ECCV 2024] GeoCalib: Learning Single-image Calibration with Geometric Optimization](/img/tr.png)
![“อ.เบียร์ คนตื่นธรรม” มาตามคำเรียกร้อง แหกศาสตร์ฮวงจุ้ย เตือนสติอย่างมงาย | แฉ 7 พ.ย. 67 [1/3]](http://i.ytimg.com/vi/5fc4S3xBNfc/mqdefault.jpg)
![[CVPR 2023] OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](/img/n.gif)
Can't wait to see a software implementation of this!
Excellent work!
WOW!!! I will be shocked if they did not give you the Marr prize.
very interesting work, the output is very clean
how does it deal with lens distortion? is it compensated by the FBA where the sample with the least distortion gets chosen as the reference? or does the FBA internally have some estimation of the distortion and applies that to all keypoints from the same sample?
awesome work😃
6啊老铁
Awesome work!