My face


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



PitaPata Cat tickers

Hang Su

I’m a 5th year PhD student at UMass-Amherst. I am co-advised by Prof. Erik Learned-Miller and Prof. Subhransu Maji at the UMass Vision Lab. 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 graphics. Below you can find some of my research projects.


SPLATNet: Sparse Lattice Networks for Point Cloud Processing


A network architecture that efficiently operates on a sparse set of samples in a high-dimensional lattice.

[project page] [video] [arXiv] [code] (to be available)

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, to appear).

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


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.


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

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.


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.

My Calendar