activity
20162023
most citedFitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences

88 citations · 183 across the 14 of their papers we have counts for

collaborators

19 papers

cs.CV2023

ID2image: Leakage of non-ID information into face descriptors and inversion from descriptors to images

Mingrui Li, William A. P. Smith, Patrik Huber

Embedding a face image to a descriptor vector using a deep CNN is a widely used technique in face recognition. Via several possible training strategies, such embeddings are suppose…

cs.CV2023

If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval

Finlay G. C. Hudson, William A. P. Smith

In this paper, we introduce a novel method for zero-shot, cross-domain image retrieval. Our key contribution is a test-time Iterative Cluster-free Re-ranking process that leverages…

cs.CV2022

Neural apparent BRDF fields for multiview photometric stereo

Meghna Asthana, William A. P. Smith, Patrik Huber

We propose to tackle the multiview photometric stereo problem using an extension of Neural Radiance Fields (NeRFs), conditioned on light source direction. The geometric part of our…

cs.CV2022★ 5 cited

Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior

James A. D. Gardner, Bernhard Egger, William A. P. Smith

Inverse rendering is an ill-posed problem. Previous work has sought to resolve this by focussing on priors for object or scene shape or appearance. In this work, we instead focus o…

cs.CV2021

Self-supervised Outdoor Scene Relighting

Ye Yu, Abhimitra Meka, Mohamed Elgharib +3

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, req…

cs.CV2021

Outdoor inverse rendering from a single image using multiview self-supervision

Ye Yu, William A. P. Smith

In this paper we show how to perform scene-level inverse rendering to recover shape, reflectance and lighting from a single, uncontrolled image using a fully convolutional neural n…