colmap-fusion-gps

Completion and Annotations : Improved COLMAP

colmap fusion gps ,reduce drift (Submitted in 2021)
feature scale determines the weight of BA
multi-data, such as depth and distance constrained BA
IDWM for two-view triangulation
Panoramic data splitting code (branch:postprocess_panorama)
Added panoramic camera model
learned-feature sfm builds long-term map
Map localization based on SIFT
There will be more improvements later, and these are just the tip of the iceberg I showed (my motto is that explaining the source code is meaningless, only improvements are valuable work, long live photogrammetry!)

3年多前colmap fusion gps 完整代码，二期课程代码会基于这个branch
（1） feature scale 定权加入BA

（2）多模态数据，如深度、距离约束BA

测试数据：https://drive.google.com/file/d/1GSZ9oPpWhCJ1ycLcv47_ZeWBXhFYUO8Z/view

见fusion-gps branch 分支

colmap fusion gps ,reduce drift !

this is original colmap run forward-facing data,scale drift !!!!!!!!!!!!!!!!!!

I add gps constraint in global bundle adjustment,obviously，the drift disappear！

I have simulated tunnels or no GPS, and my integrated GPS still performs well，like this!

常用数据集

1、ISPRS
https://www.isprs.org/education/benchmarks.aspx
2、sensefly
https://www.sensefly.com/education/datasets/
3、Kagaru
https://michaelwarren.info/docs/datasets/kagaru-airborne-stereo/
4、WHU
http://gpcv.whu.edu.cn/data/WHU_MVS_Stereo_dataset.html
5、KITTI
http://www.cvlibs.net/datasets/kitti/
6、ETH
https://www.eth3d.net/
7、realitycapture
https://www.capturingreality.com/SampleDatasets
8、视觉定位数据集
https://www.visuallocalization.net/datasets/
9、ODM
https://www.opendronemap.org/odm/datasets/
10、mve 官网的数据
https://www.gcc.tu-darmstadt.de/home/proj/ambient_point_clouds/apc.en.jsp
11、奥地利维也纳理工大学的Harvest4D项目(有GT）
https://harvest4d.org/?page_id=1367
12、自动化所偏古建筑
http://vision.ia.ac.cn/zh/data/index.html
13、blendedmvs
https://github.com/YoYo000/BlendedMVS
14、Drone Mapping
https://dronemapper.com/sample_data/
16、https://www.cs.cornell.edu/projects/bigsfm/#code bigsfm
17、http://brian.harrison.org/Photogrammetry/JemezHistoricalSite/BodyJemez.html
Last, thank you very much for the excellent work of the author(@ahojnnes) of colmap.Later,I will add feature scale constraint in bundle adjustment!!!!!