Hi Pau,
Thanks for your answer.
1: As suggested by the paper listed, fisheye images are classified as “bad images” that deteriorate the SFM performance. Does openSFM come with modifications to address this problem?
2: I am also interested in the image related problems faced by Mapillary applying OpenSFM in general. That would help me to grasp a better understanding of its functionalityies and boundaries.
3: “If the images are taken from different heights, the final model might appear tilted with respect to the ground.” Does that mean that the image paramters “camera center” would show estimated 3D coordinate?
4: Is there an extension to speed up OpenSFM? To my knowledge, some parts of the algorithm implementation can be parellized. The current implementation is time-costly in my trial experience with openSFM.
5: As the paper suggested, the GPS coordinate are mainly used for Geo registration, not for improving the SFM model. However, the github says “It also integrates external sensor (e.g. GPS, accelerometer) measurements for geographical alignment and robustness”. Could you help explain how the OpenSFM uses the GPS and accelerometer to improve the robustness of OpenSFM? I ran an experiment with modified random GPS (in EXIF) and the results turn out to be fine.
6: How do Mapillary incrementally manage newly updated photos using OpenSFM? Do we consider them as an “incremental images” to the previously built SFM model? How do we resolve the conflict of images taken from the same viewpoint but different time that the content in the image has substantially changed?
7: Does the provided GPS coordinate help (1) speed up pair-wise feature matching? (2) used as rigid or non-rigid prior for bundle adjustment?
Thank you very much Pau. Appreciate that.
Best,
Peyton