Peiyun Hu

I am a 5th year PhD student at the Robotics Institute of Carnegie Mellon University. I am fortunate to be advised by Deva Ramanan. I also work closely with David Held. Prior to CMU, I spent 2 years at UC Irvine and 4 years at Tsinghua University.

I will graduate in summer 2021 and am looking for research scientist and postdoc positions.

Research

My research focuses on computer vision, often motivated by the task of developing perception systems for autonomous robots. My work uses machine learning and deep learning to improve the robustness and scalability of learning-based perception, often led by three questions:

What is a good representation of 2D and 3D sensor data for solving perception tasks?

How can we learn more with less (human supervision)?

What is a good output representation of perception for supporting downstream applications?

Below are some of the projects where we delve into these questions.

	Safe Local Motion Planning with Self-Supervised Freespace Forecasting Peiyun Hu, Aaron Huang, John Dolan, David Held, Deva Ramanan Computer Vision and Pattern Recognition (CVPR), 2021 Coming soon!
	Active Perception using Light Curtains for Autonomous Driving Siddharth Ancha, Yaadhav Raaj, Peiyun Hu, Srinivasa Narasimhan, David Held European Conference on Computer Vision (ECCV), 2020 (Spotlight Presentation) paper / project / slides / video / code
	What You See is What You Get: Exploiting Visibility for 3D Object Detection Peiyun Hu, Jason Ziglar, David Held, Deva Ramanan Computer Vision and Pattern Recognition (CVPR), 2020 (Oral Presentation) paper / project / slides / talk / video / code We exploit often overlooked freespace in LiDAR-based 3D object detection.
	Learning to Optimally Segment Point Clouds Peiyun Hu, David Held, Deva Ramanan IEEE Robotics and Automation Letters (RA-L) and ICRA, 2020 paper / project / slides / talk / video / code We marry graph search with learning for point cloud optimal segmentation.
	Recognizing Tiny Faces Siva Chaitanya Mynepalli, Peiyun Hu, Deva Ramanan Computer Vision and Pattern Recognition Workshops (CVPR-W), 2019 paper
	Inferring Distributions Over Depth from a Single Image Gengshan Yang, Peiyun Hu, Deva Ramanan IEEE International Conference on Intelligent Robots and Systems (IROS), 2019 paper / project / slides / code
	Active Learning with Partial Feedback Peiyun Hu, Zack C. Lipton, Anima Anandkumar, Deva Ramanan International Conference on Learning Representations (ICLR), 2019 paper / poster / code We formulate AL as a 20Q game between evolving models and human.
	Camera-based Semantic Enhanced Vehicle Segmentation for Planar LIDAR Chen Fu, Peiyun Hu, Chiyu Dong, Christoph Mertz, John Dolan International Conference on Intelligent Transportation Systems (ISTC), 2018 paper
	Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset Zachary Pezzementi, Trenton Tabor, Peiyun Hu, Jonathan K. Chang, Deva Ramanan, Carl Wellington, Benzun P. Wisely Babu, Herman Herman Journal of Field Robotics (JFR), 2018 paper / project / video
	Unconstrained Face Detection and Open-Set Face Recognition Challenge Manuel Gunther, Peiyun Hu, Christian Herrmann, Chi-Ho Chan, Min Jiang, Shufan Yang, Akshay Raj Dhamija, Deva Ramanan, Jurgen Beyerer, Josef Kittler, Mohamad Al Jazaery, Mohammad Iqbal Nouyed, Guodong Guo, Cezary Stankiewicz, Terrance E Boult IEEE International Joint Conference on Biometrics (IJCB), 2017 paper
	Finding Tiny Faces Peiyun Hu, Deva Ramanan Computer Vision and Pattern Recognition (CVPR), 2017 paper / project / video / poster / press / code We address key challenges in detecting tiny objects with neural nets.
	Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians Peiyun Hu, Deva Ramanan Computer Vision and Pattern Recognition (CVPR), 2016 (Spotlight Presentation) paper / project / ext. abstract / slides / talk / poster / code We derive architectures to endow neural nets with top-down reasoning.

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