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256 CS Bldg.
140 Governors Dr.
Amherst, MA 01003
hsu _at_
(401) 284-7656





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Hang Su

I’m currently a 3rd year PhD student at UMass-Amherst. I got my master’s degree in Compute Science from Brown University and my bachelor’s degree in Intelligent Science and Technology from Peking University back in China.

My major research interests lie in computer vision and machine learning. Below you can find some of my related projects.


Multi-view CNN (MVCNN) for 3D Shape Recognition 2014-Current

MVCNN architecture

A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?

We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images. We present 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. [page]


Hang Su, Subhransu Maji, Evangelos Kalogerakis, Erik Learned-Miller, "Multi-view Convolutional Neural Networks for 3D Shape Recognition", Proceedings of 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

Layered Global-Local (GLOC) Model for Image Parts Labelling with Occlusion 2013-Current

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.

Scene Attributes 2012-2013

Scene Attributes


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

Face & Pose Detection Using Deformable Part-based Model 2012 Summer

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). This C++ implementation achieved 0.95 recall and 0.90 precision on eHarmony’s user profile photos.

Code and documentation are available.

Photo Quality Assessment: Focused on User Profile Photos 2012

Photo Quality Assessment

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

Here I focus on assessing the quality of photos that contain faces (e.g. user profile photos). I propose several features specially useful for this task, e.g. skin smoothness, composition, bokeh, etc. Experiments show that with some small modifications they are also very useful for assessing other types of photos. [report]

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 extensive application prospect.

This project is focused on the development of a front vehicle detection system using onboard camera. Hypothesis generation based on shadows and hypothesis verification based on HOG features are integrated to achieve a real-time system. A passing vehicle detection component using optical flow is also proposed, and obtains fast and reliable detections.

3D Modelling of Peking University Campus 2008

One of the 3D models

With nearly a hundred beautifully built 3D models of Peking University, our team won the top prize in 2008 International Modelling Your Campus competition hosted by Google.

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