Yu-Ting Wu (吳昱霆)
Postdoctoral Researcher

Communication and Multimedia Laboratory (CMLab)
National Taiwan University

kevincosnerwu [at] gmail.com

Curriculum Vitae


Recent News

Time Event
June 2021 Served as a reviewer for ICCV 2021
May 2021 Our paper, "Multi-Resolution Shared Representative Filtering for Real-Time Depth Completion", is accepted by High-Performance Graphics 2021
Mar. 2021 Served as a reviewer for APMAR 2021
Dec. 2020 Our paper, "ClipFlip: Multi-view Clipart Design", is accepted to Computer Graphics Forum
Dec. 2020 Served as a reviewer for CVPR 2021
Sep. 2020 My son was born!
May 2020 Served as a reviewer for ECCV 2020
Feb. 2020 Joined Communication and Multimedia Laboratory (CMLab) of National Taiwan University as a postdoctoral researcher

Biography

I am currently a postdoctoral researcher at Communication and Multimedia Laboratory (CMLab), National Taiwan University. I recieved my bachelor's degree and master's degree in National Chiao Tung University (NCTU) in 2007 and 2009, respectively. After that, I continued my research with Prof. Yung-Yu Chuang in National Taiwan University (NTU) and defended my Ph.D. degree in 2014. During the period 2014 to 2019, I worked as a software engineer at HTC and Toppano, mainly in charge of developing computer graphics and computational photography techniques for VR and MR applications. My research interests include computer graphics, computational photography, computer vision, AR/MR/VR , and machine learning.


Experience

Postdoctoral Researcher - National Taiwan University, Feb. 2020 - Present

Senior Algorithm Developer - Toppano Inc. (a startup company), May 2018 - Jan. 2020

Principal Engineer - HTC Inc., Sep. 2014 - Apr. 2018

Summer Intern - Digimax Inc., Jul. 2011 - Sep. 2011

Teaching Assistant (Digital Image Synthesis) - National Taiwan University, Fall 2009 - 2013

Teaching Assistant (Computer Graphics) - National Chiao Tung University, Fall 2008


Publications

Categorized by:   Computer Graphics CG   Computer Vision CV   Image Processing IP   Machine Learning ML  

Multi-Resolution Shared Representative Filtering for Real-Time Depth Completion   CG CV IP
Yu-Ting Wu, Tzu-Mao Li, I-Chao Shen, Hong-Shiang Lin, Yung-Yu Chuang
Accepted to High-Performance Graphics 2021
(a real-time depth completion algorithm which can effectively handle large missing regions of depth maps)

Coming Soon


ClipFlip: Multi-view Clipart Design   CG CV ML
I-Chao Shen, Kuan-Hung Liu, Li-Wen Su, Yu-Ting Wu, Bing-Yu Chen
Computer Graphics Forum, February 2021
(an assistive system for clipart design by providing visual scaffolds from the unseen view points)

     We present an assistive system for clipart design by providing visual scaffolds from the unseen viewpoints. Inspired by the artists’ creation process, our system constructs the visual scaffold by first synthesizing the reference 3D shape of the input clipart and rendering it from the desired viewpoint. The critical challenge of constructing this visual scaffold is to generate a reference 3D shape that matches the user’s expectations in terms of object sizing and positioning while preserving the geometric style of the input clipart. To address this challenge, we propose a user-assisted curve extrusion method to obtain the reference 3D shape. We render the synthesized reference 3D shape with a consistent style into the visual scaffold. By following the generated visual scaffold, the users can efficiently design clipart with their desired viewpoints. The user study conducted by an intuitive user interface and our generated visual scaffold suggests that our system is especially useful for estimating the ratio and scale between object parts and can save on average 57% of drawing time.

Project Page Paper Citation arXiv version Digital Library

Dual-Matrix Sampling for Scalable Translucent Material Rendering   CG
Yu-Ting Wu, Tzu-Mao Li, Yu-Hsun Lin, Yung-Yu Chuang
IEEE Transactions on Visualization and Computer Graphics, March 2015
(a scalable algorithm for rendering plenty of translucent objects under complex illumination)

     This paper introduces a scalable algorithm for rendering translucent materials with complex lighting. We represent the light transport with a diffusion approximation by a dual-matrix representation with the Light-to-Surface and Surface-to-Camera matrices. By exploiting the structures within the matrices, the proposed method can locate surface samples with little contribution by using only subsampled matrices and avoid wasting computation on these samples. The decoupled estimation of irradiance and diffuse BSSRDFs also allows us to have a tight error bound, making the adaptive diffusion approximation more efficient and accurate. Experiments show that our method outperforms previous methods for translucent material rendering, especially in large scenes with massive translucent surfaces shaded by complex illumination.

Project Page Paper Citation Digital Library

VisibilityCluster: Average Directional Visibility for Many-Light Rendering   CG
Yu-Ting Wu, Yung-Yu Chuang
IEEE Transactions on Visualization and Computer Graphics, September 2013
(a method for efficient computation and compact representation of the visibility function for many-light rendering)

     This paper proposes the VisibilityCluster algorithm for efficient visibility approximation and representation in many-light rendering. By carefully clustering lights and shading points, we can construct a visibility matrix that exhibits good local structures due to visibility coherence of nearby lights and shading points. Average visibility can be efficiently estimated by exploiting the sparse structure of the matrix and shooting only few shadow rays between clusters. Moreover, we can use the estimated average visibility as a quality measure for visibility estimation, enabling us to locally refine VisibilityClusters with large visibility variance for improving accuracy. We demonstrate that, with the proposed method, visibility can be incorporated into importance sampling at a reasonable cost for the manylight problem, significantly reducing variance in Monte Carlo rendering. In addition, the proposed method can be used to increase realism of local shading by adding directional occlusion effects. Experiments show that the proposed technique outperforms state-ofthe-art importance sampling algorithms, and successfully enhances the preview quality for lighting design.

Project Page Paper Citation Digital Library

SURE-based Optimization for Adaptive Sampling and Reconstruction   CG
Tzu-Mao Li, Yu-Ting Wu, Yung-Yu Chuang
ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012), selected as a highlight paper by the program chair
(an adaptive sampling and denoising method for Monte Carlo rendering using Stein's unbiased risk estimator)

     We apply Stein’s Unbiased Risk Estimator (SURE) to adaptive sampling and reconstruction to reduce noise in Monte Carlo rendering. SURE is a general unbiased estimator for mean squared error (MSE) in statistics. With SURE, we are able to estimate error for an arbitrary reconstruction kernel, enabling us to use more effective kernels rather than being restricted to the symmetric ones used in previous work. It also allows us to allocate more samples to areas with higher estimated MSE. Adaptive sampling and reconstruction can therefore be processed within an optimization framework. We also propose an efficient and memory-friendly approach to reduce the impact of noisy geometry features where there is depth of field or motion blur. Experiments show that our method produces images with less noise and crisper details than previous methods.

Project Page Paper Slides Citation Digital Library

Short Papers and Posters

VisibilityChuck: Average Directional Visibility for Importance Sampling   CG
Yu-Ting Wu, Yung-Yu Chuang
ACM SIGGRAPH Asia 2012 Poster, selected as a highlight poster by the program chair
(an early version of our VisibilityCluster paper)

Paper Digital Library

Ph.D. Dissertation

Sampling and Reconstruction Techniques for Efficient Monte Carlo Rendering   CG
Yu-Ting Wu, advised by Yung-Yu Chuang
Doctor of Philosophy in Computer Science and Information Engineering, National Taiwan University, June 2014
(a coherent view of my Ph.D. research with background reviews)

     Two of the most important tasks that computer graphics techniques try to solve is rendering photo-realistic images and performing numerically accurate simulation. Physically-based rendering can naturally satisfy these two goals. It is usually simulated by the Monte Carlo ray tracing for handling a variety of sophisticated light transport paths in a united manner. Despite its generality and simplicity, however, Monte Carlo integration converges slowly. Rendering scenes with lots of complex geometry and realistic materials under complex illumination usually requires a large number of samples to produce a noise-free image.
     In this dissertation, we proposed three advanced sampling and reconstruction algorithms for improving the performance of Monte Carlo integration. First, realizing that in complex scenes visibility is usually the major source of noise during sampling the shading function, we developed a method called VisibilityCluster for efficiently approximating visibility function. By integrating it into importance sampling framework, we achieve superior noise reduction compared to previous approaches. Second, to reduce the computation overhead of rendering translucent materials, we proposed an algorithm, Dualmatrix sampling, to avoid evaluating unimportant surface samples which contribute little to the final image. Finally, a general adaptive sampling and reconstruction framework named SURE-based optimization is proposed to render a wide range of distributed effects, including depth of field, motion blur, and global illumination. All of the three methods achieve significant performance improvement compared to the state-of-the-art rendering algorithms.

Dissertation Slides

Patents

Electronic device, method for displaying an augmented reality scene and non-transitory computer-readable medium   CG CV
Yu-Ting Wu, Ching-Yang Chen
ROC Patent No: I711966. December 01, 2020
US Patent No: 10636200, April 28, 2020

Virtual reality device, image processing method, and non-transitory computer-readable medium   CG
Yu-Ting Wu, Chun-Wen Cheng, Ching-Yang Chen
ROC Patent No: I684163, February 01, 2020

Three-dimensional modeling method and electronic apparatus thereof   CG CV
Sheng-Jie Luo, Liang-Kang Huang, Yu-Ting Wu, Tung-Peng Wu
US Patent No: 10152827, December 11, 2018





The design of this webpage is borrowed from Dr. Lingqi Yan

Last Update: May 2021