I have a set of GelSight images and need an end-to-end workflow that turns them into accurate, high-resolution 3D meshes of everyday objects. The goal is to capture subtle surface variations—scratches, fine textures, and edge curvature—so the final model can be inspected, measured, and exported for downstream use in CAD or visualization tools. You may choose whichever environment you are most comfortable with (Python + OpenCV/PyTorch, MATLAB, or C++). What matters is that the pipeline: • takes raw GelSight data as input (I will supply sample files), • performs all necessary photometric calibration and image alignment, • reconstructs a dense height map, then converts it to a watertight mesh, • outputs OBJ or STL alongside a brief report on reconstruction quality. Results should preserve features down to roughly 10 µm where the source data supports it; I will verify by comparing cross-sections against a reference profile. Please include an outline of your planned approach, example of a similar reconstruction you have completed, and the estimated turnaround time for a first working prototype.