Towards Understanding 3D Vision: the Role of Gaussian Curvature

Recent advances in computer vision have predominantly relied on data-driven approaches that leverage deep learning and large-scale datasets. Deep neural networks have achieved remarkable success in tasks such as stereo matching and monocular depth reconstruction. However, these methods lack explicit models of 3D geometry that can be directly analyzed, transferred across modalities, or systematically modified for controlled experimentation. We investigate the role of Gaussian curvature in 3D surface modeling. Besides Gaussian curvature being an invariant quantity under change of observers or coordinate systems, we demonstrate using the Middlebury stereo dataset that it offers: (i) a sparse and compact description of 3D surfaces, (ii) state-of-the-art monocular and stereo methods seem to implicitly consider it, but no explicit module of such use can be extracted, (iii) a form of geometric prior that can inform and improve 3D surface reconstruction, and (iv) a possible use as an unsupervised metric for stereo methods.