Hello,
we're currently looking at using GTSAM as part of our 3D reconstruction pipeline. I am wondering if there's a way to provide lots (millions) of pixelwise measurements to GTSAM in an efficient manner.
To give this a bit more color: Let's say we optimize a set of 6dof camera poses. We have lots (many millions) of pixelwise terms / measurements that each provide a constraint on a pose-pose pair. Today, we assemble the JTJ matrix and the JTr vector all internally using fast optimized code and then solve using an off the shelf solver.
So far, so good, but there are many variations of that problem that we're looking at (having additional non-pixelwise constraints, hard constraints, extensions to the state vector, etc.) where GTSAM would come in very handy.
However I'm not sure there's a way to provide millions of pixelwise constraints to GTSAM in an efficient manner as it seems to target use cases with relatively few (1000s or 10000s) measurements, such as from feature-based SLAM.
Could we, for example, still precompute the relevant parts of JTJ and JTr internally and then provide this to GTSAM as a "precomputed block of information"?
Any other suggestions are welcome too!
Regards,
Rafael