167 citations · 737 across the 33 of their papers we have counts for
6 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…
On progressive sharpening, flat minima and generalisation
Lachlan Ewen MacDonald, Jack Valmadre, Simon Lucey
We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning. Specifically, we use existing empirical analys…
Fast Neural Scene Flow
Xueqian Li, Jianqiao Zheng, Francesco Ferroni +2
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with d…
Re-Evaluating LiDAR Scene Flow for Autonomous Driving
Nathaniel Chodosh, Deva Ramanan, Simon Lucey
Popular benchmarks for self-supervised LiDAR scene flow (stereoKITTI, and FlyingThings3D) have unrealistic rates of dynamic motion, unrealistic correspondences, and unrealistic sam…
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…
On the effectiveness of neural priors in modeling dynamical systems
Sameera Ramasinghe, Hemanth Saratchandran, Violetta Shevchenko +1
Modelling dynamical systems is an integral component for understanding the natural world. To this end, neural networks are becoming an increasingly popular candidate owing to their…