Multi-view Image-based Hand Geometry Refinement using Differentiable Monte Carlo Ray Tracing

The amount and quality of datasets and tools available in the research field of hand pose and shape estimation act as evidence to the significant progress that has been made. We find that there is still room for improvement in both fronts, and even beyond. Even the datasets of the highest quality, reported to date, have shortcomings in annotation. There are tools in the literature that can assist in that direction and yet they have not been considered, so far. To demonstrate how these gaps can be bridged, we employ such a publicly available, multi-camera dataset of hands (InterHands), and perform effective image-based refinement to improve on the imperfect ground truth annotations, yielding a better dataset. The image-based refinement is achieved through raytracing, a method that has not been employed so far to relevant problems and is hereby shown to be superior to the approximative alternatives that have been employed in the past. To tackle the lack of reliable ground truth, we resort to realistic synthetic data, to show that the improvement we induce is indeed significant, qualitatively, and quantitatively, too.