Go to OpenReview Archive homepageMillimetric Human Surface Capture in Minutes
Detailed human surface capture from multiple images is an essential component for many 3D production, analysis and transmission tasks. Yet producing millimetric precision 3D models in practical time, and actually verifying their 3D accuracy in a real-world capture context, remain key challenges due to the lack of specific methods and data for these goals. We propose two complementary contributions to this end. The first one is a highly scalable neural surface radiance field approach able to achieve millimetric precision by construction, while demonstrating high compute and memory efficiency. The second one is a novel dataset of clothed mannequin geometry captured with a high resolution hand-held 3D scanner paired with calibrated multiview images, that allow to verify the millimetric accuracy claim. Although our approach can produce such a highly dense and precise geometry, we show how aggressive sparsification and optimizations of the neural surface pipeline lead to estimations requiring only minutes of computation time and a few GB of VRAM memory on GPU, while allowing for real-time millisecond neural rendering. On the basis of our framework and dataset, we provide a thorough experimental analysis of how such accuracies and efficiencies are achieved in the context of multi-camera human acquisition.