120 citations · 425 across the 22 of their papers we have counts for
7 papers · 1 filter
Object DGCNN: 3D Object Detection using Dynamic Graphs
Yue Wang, Justin Solomon
3D object detection often involves complicated training and testing pipelines, which require substantial domain knowledge about individual datasets. Inspired by recent non-maximum…
MarioNette: Self-Supervised Sprite Learning
Dmitriy Smirnov, Michael Gharbi, Matthew Fisher +3
Artists and video game designers often construct 2D animations using libraries of sprites -- textured patches of objects and characters. We propose a deep learning approach that de…
Multi-Frame to Single-Frame: Knowledge Distillation for 3D Object Detection
Yue Wang, Alireza Fathi, Jiajun Wu +2
A common dilemma in 3D object detection for autonomous driving is that high-quality, dense point clouds are only available during training, but not testing. We use knowledge distil…
Pillar-based Object Detection for Autonomous Driving
Yue Wang, Alireza Fathi, Abhijit Kundu +4
We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse…
Polygonal Building Segmentation by Frame Field Learning
Nicolas Girard, Dmitriy Smirnov, Justin Solomon +1
While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To h…
Deep Closest Point: Learning Representations for Point Cloud Registration
Yue Wang, Justin M. Solomon
Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications. This problem involves finding a rigid transformation fro…