36 citations · 95 across the 13 of their papers we have counts for
9 papers · 1 filter
Curvature-Aware Training for Coordinate Networks
Hemanth Saratchandran, Shin-Fang Chng, Sameera Ramasinghe +2
Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities. However, training these networks with first-ord…
Flow supervision for Deformable NeRF
Chaoyang Wang, Lachlan Ewen MacDonald, Laszlo A. Jeni +1
In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision. We overcome the major challenge with respect to the computationally ine…
Fast, Dense Feature SDM on an iPhone
Ashton Fagg, Simon Lucey, Sridha Sridharan
In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a…
Inverse Compositional Spatial Transformer Networks
Chen-Hsuan Lin, Simon Lucey
In this paper, we establish a theoretical connection between the classical Lucas & Kanade (LK) algorithm and the emerging topic of Spatial Transformer Networks (STNs). STNs are of…
Regression-Based Image Alignment for General Object Categories
Hilton Bristow, Simon Lucey
Gradient-descent methods have exhibited fast and reliable performance for image alignment in the facial domain, but have largely been ignored by the broader vision community. They…
Why do linear SVMs trained on HOG features perform so well?
Hilton Bristow, Simon Lucey
Linear Support Vector Machines trained on HOG features are now a de facto standard across many visual perception tasks. Their popularisation can largely be attributed to the step-c…