8 citations · 17 across the 5 of their papers we have counts for
7 papers
Lidar Panoptic Segmentation in an Open World
Anirudh S Chakravarthy, Meghana Reddy Ganesina, Peiyun Hu +4
Addressing Lidar Panoptic Segmentation (LPS ) is crucial for safe deployment of autonomous vehicles. LPS aims to recognize and segment lidar points w.r.t. a pre-defined vocabulary…
Differentiable Raycasting for Self-supervised Occupancy Forecasting
Tarasha Khurana, Peiyun Hu, Achal Dave +3
Motion planning for safe autonomous driving requires learning how the environment around an ego-vehicle evolves with time. Ego-centric perception of driveable regions in a scene no…
Inferring Distributions Over Depth from a Single Image
Gengshan Yang, Peiyun Hu, Deva Ramanan
When building a geometric scene understanding system for autonomous vehicles, it is crucial to know when the system might fail. Most contemporary approaches cast the problem as dep…
Learning to Optimally Segment Point Clouds
Peiyun Hu, David Held, Deva Ramanan
We focus on the problem of class-agnostic instance segmentation of LiDAR point clouds. We propose an approach that combines graph-theoretic search with data-driven learning: it sea…
What You See is What You Get: Exploiting Visibility for 3D Object Detection
Peiyun Hu, Jason Ziglar, David Held +1
Recent advances in 3D sensing have created unique challenges for computer vision. One fundamental challenge is finding a good representation for 3D sensor data. Most popular repres…
Active Learning with Partial Feedback
Peiyun Hu, Zachary C. Lipton, Anima Anandkumar +1
While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one…