Hsueh-Ti (Derek) Liu

About

I am a first-year Computer Sciecne PhD student at University of Toronto, advised by Alec Jacobson. Before that I was a Master's student in Mechanical Engineering at Carnegie Mellon University, advised by Keenan Crane and Levent Burak Kara. My research focuses on 3D geometry processing using spectral and maching learning approaches. Previously I was an undergrad student at National Taiwan University majoring in Ocean Engineering, having experiences in ship design and fluid mechanics.
[Resume] [E-mail]


Publications

A Dirac Operator for Extrinsic Shape Analysis

Hsueh-Ti Derek Liu, Alec Jacobson, Keenan Crane

Eurographics/ACM SIGGRAPH Symposium on Geometry Processing 2017

[Paper] [Project]


Projects

Deep Adversarial 3D Shape Net

10807: Topics in Deep Learning (Fall 2016)

3D shapes are a crucial but heavily underutilized resource in current computer vision research. Recently, deep learning models for 3D shapes have started to emerge because large amount of 3D shape data have become accessible. People are actively developing/finding proper 3D shape representations, such as uniform voxel representation and multi-view images. With the help of geometric approaches, this research topic tries to answer - is there a better way to represent 3D shapes for training deep learning models? [pdf]

Combining Active Learning and Accuracy Estimation using Unlabelled Data

10701: Introduction to Machine Learning (Spring 2016)

How unlabelled data can be used to estimate the true accuracy of learned classifier is an important problem across different fields. It is counter intuitive because we usually need labelled data to compute the true accuracy. In this project, we explored Error Estimation model which use graphical model approach to capture the dependency of making error across classifiers in order to estimate true accuracy using only unlabelled data. Then we brought this information to active learning model to verify the usefulness of estimated accuracy. [pdf]

Shape Descriptor Design Using Spin Transformation

15869: Discrete Differential Geometry (Spring 2016)

A well-known theory in differential geometry that mean curvature and metric should suffice to determine a surface uniquely. However, there has been no shape descriptor constructed using mean curvature and a metric. In this project, I applied Spin Transformation, mean curvature flow, and persistent barcode to construct a shape descriptor based on this theory. [pdf]

Recognition of Partially Obstructed 3D Objects from Point Cloud Data

24681: Computer-Aided Design (Spring 2016)

In the 3D printing industry, a metal printing process called the de-powdering process requires manually removing excess powder. In this project, we developed a framework to recognize 3D shapes from partially observed surface patches. With this framework in hand, we can then use robot arms to remove excess powder, which will significantly simplify the de-powdering process. [pdf]

Fundamental Fluid Mechanics Laboratory

Undergraduate Research (2013)

In this project, the main task is to explore the phenomenon when waves propagate over a submerged bar. We conducted wave propagation experiments and used a numerical model to simulate the experimental results.

Ship Model Basin Laboratory

Undergraduate Research (2013)

Optimizing hull positions of a trimaran through conducting ship model resistance tests. Based on the results, we optimized the hull positions and predicted the ship's performance in real size.