60 citations · 60 across the 2 of their papers we have counts for
5 papers
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…
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…
PointNetLK Revisited
Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey
We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied…
Deep Level Sets: Implicit Surface Representations for 3D Shape Inference
Mateusz Michalkiewicz, Jhony K. Pontes, Dominic Jack +2
Existing 3D surface representation approaches are unable to accurately classify pixels and their orientation lying on the boundary of an object. Thus resulting in coarse representa…
Learning Free-Form Deformations for 3D Object Reconstruction
Dominic Jack, Jhony K. Pontes, Sridha Sridharan +4
Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing…