167 citations · 736 across the 30 of their papers we have counts for
10 papers · 1 filter
Architectural Adversarial Robustness: The Case for Deep Pursuit
George Cazenavette, Calvin Murdock, Simon Lucey
Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of…
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…
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey
Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the…
MaskNet: A Fully-Convolutional Network to Estimate Inlier Points
Vinit Sarode, Animesh Dhagat, Rangaprasad Arun Srivatsan +3
Point clouds have grown in importance in the way computers perceive the world. From LIDAR sensors in autonomous cars and drones to the time of flight and stereo vision systems in o…
Joint Pose and Shape Estimation of Vehicles from LiDAR Data
Hunter Goforth, Xiaoyan Hu, Michael Happold +1
We address the problem of estimating the pose and shape of vehicles from LiDAR scans, a common problem faced by the autonomous vehicle community. Recent work has tended to address…
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…