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Contact

Office
256 CS Bldg.
140 Governors Dr.
Amherst, MA 01003
Email
hsu _at_ cs.umass.edu

Links

Meet Eclipse!

PitaPata Cat tickers

Hang Su

I'm a 5th year PhD student in the Computer Vision Lab at UMass Amherst, advised by Prof. Erik Learned-Miller and Prof. Subhransu Maji. I work in the areas of computer vision and computer graphics, and in particular, I am interested in bringing together the strengths of 2D and 3D visual information for learning richer and more flexible representations. I obtained my master's degree from Brown University and my bachelor's degree from Peking University.

Publications

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

SPLATNET

A fast and end-to-end trainable neural network that directly works on point clouds and can also do joint 2D-3D processing.

Awarded "Best Paper Honorable Mention" at CVPR'18!

NVAIL Pioneering Research Award

[project page] [video] [arXiv] [GitHub]


Hang Su, Varun Jampani, Deqing Sun, Subhransu Maji, Evangelos Kalogerakis, Ming-Hsuan Yang, and Jan Kautz, "SPLATNet: Sparse Lattice Networks for Point Cloud Processing", CVPR 2018 (oral).

End-to-end Face Detection and Cast Grouping in Movies Using Erdős–Rényi Clustering

erdos-renyi

An end-to-end system for detecting and clustering faces by identity in full-length movies.

[project page] [pdf] [code]


SouYoung Jin, Hang Su, Chris Stauffer, and Erik Learned-Miller, "End-to-end face detection and cast grouping in movies using Erdős–Rényi clustering", ICCV 2017 (splotlight).

Multi-view CNN (MVCNN) for 3D Shape Recognition

MVCNN architecture

A novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering state-of-the-art performance in a range of recognition tasks.

Ranked #1 in a SHREC'16 contest!

[video] [project page] [pdf] [code]


Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller, "Multi-view Convolutional Neural Networks for 3D Shape Recognition", ICCV 2015.

M. Savva, F. Yu, H. Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, H. Su, S. Bai, X. Bai, N. Fish, J. Han, E. Kalogerakis, E. G. Learned-Miller, Y. Li, M. Liao, S. Maji, A. Tatsuma, Y. Wang, N. Zhang, and Z. Zhou, "SHREC’16 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55", Eurographics Workshop on 3D Object Retrieval, J. Jorge and M. Lin, editors, 2016.

The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding

Scene Attributes

The first large-scale scene attribute database.

[pdf]


G. Patterson, C. Xu, H. Su, J. Hays, "The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding", IJCV, May 2014.

Earlier Projects

Layered Global-Local (GLOC) Model for Image Parts Labelling with Occlusion 2014

Global-Local Occlusion Model

Learning and reasoning visual occlusions (e.g. on faces) using a deep graphical model. Co-advised by Professor Vangelis Kalogerakis and Professor Erik Learned-Miller.

We create an extension to LFW Part Labels dataset. It provides 7 part labels to 2,927 portrait photos.

[data] (lfw-parts-v2)

Face & Pose Detection Using Deformable Part-based Model 2012 Summer (@eHarmony)

Face Detection

In this project, I implemented in C++ a human face and body detection system based on the paper "Face detection, pose estimation and landmark localization in the wild" (X. Zhu and D. Ramanan, CVPR 2012). The implementation achieves 0.95 recall and 0.90 precision on eHarmony’s user profile photos.

[code]

Photo Quality Assessment on User Profile Photos 2012

Photo Quality Assessment

The goal of this project is to automatically distinguish high quality professional photos from low quality snapshots.

We focus on assessing the quality of photos which contain faces (e.g. user profile photos). We propose several image features particularly useful for this task, e.g. skin smoothness, composition, bokeh. Experiments show that with small modifications they are also useful for assessing other types of photos.

Front Vehicle Detection Using Onboard Camera 2010-2011

Vehicle Detection & Road Segmentation

Onboard vehicle detection plays a key role in collision prevention and autonomous driving. Camera-based detection techniques have been proven effective and economical, and show wide application prospect.

This project focuses on front vehicle detection using onboard cameras. Hypothesis generation based on shadows and hypothesis verification based on HOG features are combined to achieve a real-time system. We also introduce and integrate a passing vehicle detection component using optical flow, as well as road surface segmentation.

3D Modelling of Peking University Campus 2008

One of the 3D models

With almost 100 beautifully modeled 3D buildings on Peking University campus, our team won the top prize in 2008 Google International Model Your Campus Competition.

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