167 citations · 736 across the 30 of their papers we have counts for
39 papers · 1 filter
Neural Scene Flow Prior
Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
Before the deep learning revolution, many perception algorithms were based on runtime optimization in conjunction with a strong prior/regularization penalty. A prime example of thi…
On the Bias Against Inductive Biases
George Cazenavette, Simon Lucey
Borrowing from the transformer models that revolutionized the field of natural language processing, self-supervised feature learning for visual tasks has also seen state-of-the-art…
Neural Trajectory Fields for Dynamic Novel View Synthesis
Chaoyang Wang, Ben Eckart, Simon Lucey +1
Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes. The ability to rec…
BARF: Bundle-Adjusting Neural Radiance Fields
Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba +1
Neural Radiance Fields (NeRF) have recently gained a surge of interest within the computer vision community for its power to synthesize photorealistic novel views of real-world sce…
PAUL: Procrustean Autoencoder for Unsupervised Lifting
Chaoyang Wang, Simon Lucey
Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could…
Scene Flow from Point Clouds with or without Learning
Jhony Kaesemodel Pontes, James Hays, Simon Lucey
Scene flow is the three-dimensional (3D) motion field of a scene. It provides information about the spatial arrangement and rate of change of objects in dynamic environments. Curre…