42 citations · 130 across the 12 of their papers we have counts for
12 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…
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting
Tarasha Khurana, Peiyun Hu, David Held +1
Predicting how the world can evolve in the future is crucial for motion planning in autonomous systems. Classical methods are limited because they rely on costly human annotations…
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…
Active Perception using Light Curtains for Autonomous Driving
Siddharth Ancha, Yaadhav Raaj, Peiyun Hu +2
Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this w…
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…
Unconstrained Face Detection and Open-Set Face Recognition Challenge
Manuel Günther, Peiyun Hu, Christian Herrmann +12
Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automat…
Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset
Zachary Pezzementi, Trenton Tabor, Peiyun Hu +5
Person detection from vehicles has made rapid progress recently with the advent of multiple highquality datasets of urban and highway driving, yet no large-scale benchmark is avail…
Finding Tiny Faces
Peiyun Hu, Deva Ramanan
Though tremendous strides have been made in object recognition, one of the remaining open challenges is detecting small objects. We explore three aspects of the problem in the cont…
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians
Peiyun Hu, Deva Ramanan
Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a unidirectional bottom-up feed-forward fashion. Howeve…