120 citations · 171 across the 26 of their papers we have counts for
5 papers · 1 filter
Continuous Surface Embeddings
Natalia Neverova, David Novotny, Vasil Khalidov +3
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories. While this problem has been considered before, solutions so f…
RidgeSfM: Structure from Motion via Robust Pairwise Matching Under Depth Uncertainty
Benjamin Graham, David Novotny
We consider the problem of simultaneously estimating a dense depth map and camera pose for a large set of images of an indoor scene. While classical SfM pipelines rely on a two-ste…
3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data
Benjamin Biggs, Sébastien Ehrhadt, Hanbyul Joo +3
We consider the problem of obtaining dense 3D reconstructions of humans from single and partially occluded views. In such cases, the visual evidence is usually insufficient to iden…
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
David Novotny, Roman Shapovalov, Andrea Vedaldi
We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects…
Accelerating 3D Deep Learning with PyTorch3D
Nikhila Ravi, Jeremy Reizenstein, David Novotny +4
Deep learning has significantly improved 2D image recognition. Extending into 3D may advance many new applications including autonomous vehicles, virtual and augmented reality, aut…