Yuang Shi (施宇昂)

I'm a third year Ph.D. Candidate at National University of Singapore advised by Prof. Wei Tsang Ooi, where I work on Networking and Multimedia Systems, specifically on 3D Media Streaming.

I received my M.Comp Degree in 2022 from National University of Singapore, where I did research on human activity recognition in-the-wild. I received my B. Eng. Degree in 2021 from SiChuan University in China, where I worked on Medical Image Processing.

I love cats.

Email  /  Google Scholar  /  Github

• [2024/11] - Our paper "LapisGS: Layered Progressive 3D Gaussian Splatting for Adaptive Streaming" is accepted to 3DV 2025.
• [2024/08] - Our paper "Multi-frame Bitrate Allocation of Dynamic 3D Gaussian Splatting Streaming Over Dynamic Networks" received the Best Paper Award at the SIGCOMM 2024 Workshop on Emerging Multimedia Systems (EMS 2024)!
• [2024/05] - Invited talk at Sichuan University, Sichuan.
• [2024/04] - Invited talk at Université de Toulouse, Toulouse INP-ENSEEIHT, IRIT, France.
• [2024/01] - One paper accepted to MMSys 2024.
• [2023/12] - Invited talk at National Tsing Hua University, Taiwan.

As a PhD student, I mainly focus on 3D Media Streaming, including compression, networking, and quality evaluation.

We introduce LapisGS, a layered progressive 3D Gaussian Splatting (3DGS), which offers a progressive representation supporting a continuous rendering quality adapted for bandwidth-aware streaming.

We proposed two algorithms, MGA and MGAA, to allocate bitrate across multiple 3DGS scenes for streaming over dynamic networks.

We present QV4, a Quality-of-Experience (QoE) based streaming system for viewpoint-aware V-PCC-encoded volumetric video. It is the first volumetric video streaming system that exploits user viewing adaptations on V-PCC-encoded content.

We represent 3D dynamic content as a sequence of NeRFs, converting the explicit representation to neural representation. We then compress the neural representation based on the insight of significant similarity between successive NeRFs.

We propose a QoE model to predict the subjective quality with respect to the compression level of geometry and texture, quantifying the impact of geometry and texture compression on perceptual quality.

We investigated the influence of rendering face quality of the avatars on users' viewing experience in MPEG V-PCC-encoded volumetric videos, and revealed the significant role of facial quality in influencing users' overall perceptual quality in volumetric videos.

We show that it is possible to improve the quality of V-PCC encoded point clouds at low bit-rate by exploiting redundant information among the points in the 3D domain.

We present an open dynamic 3D point cloud dataset with motion ground truth, which can be used by researchers who need temporal information across frames, e.g., motion estimation.

When I was a Master student, my dissertation is about human activity recognition in-the-wild.

ShapeCNF is a simple, fast, and accurate change-point detection method which uses shape-based features to model the patterns and a conditional neural field to model the temporal correlations among the time regions.

During my undergraduate, I spent most of my time on medical image analysis.

We propose a consistency training framework for semi-supervised NPC segmentation, which includes an Uncertainty-weighted Prediction Consistency Training (UPCT) strategy and a Relation-driven Consistency Training (RCT) strategy.

ASMFS is a novel multi-modal feature selection method for classification of Alzheimer's Disease, which performs adaptive similarity learning and feature selection simultaneously.

• [2024/08-2024/12] - Graduate Tutor & Teaching Assistant, CS3244 Machine Learning.
• [2024/01-2024/05] - Graduate Tutor & Teaching Assistant, CS3244 Machine Learning.
• [2023/01-2023/05] - Graduate Tutor & Teaching Assistant, CS3244 Machine Learning.
• [2022/08-2022/12] - Graduate Tutor & Teaching Assistant, CS3244 Machine Learning.

I really appreciate the opportunity to work with the following talent students (I only list the selected ones who I have worked with for a long time, and I have made non-negligible contributions to their research projects 😃.):

1. Jia Yi Lee.
   • FYP 2025. Efficient Video Representation with 3D Gaussian Splatting.
2. Longan Wang.
   • NGNE 2025. Gaussian-based Video Compression.
3. Sherwin Poh.
   • FYP 2024. Real-time Point Cloud Super-Resolution for Volumetric Video Streaming.
   • Contributor of VVTk, an open-sourced toolkit for volumetric video streaming.
4. Chi Sern Ng.
   • FYP 2024. Scalable Approaches to Volumetric Video Playback.
   • Contributor of VVTk, an open-sourced toolkit for volumetric video streaming.
5. Joseph Wang.
   • FYP 2024, UROP 2023. Improving the Performance of V-PCC through Segmentation With Meta's SAM.
6. Bennett Clement.
   • FYP 2023. Viewpoint-aware VPCC-Encoded Volumetric Video Streaming.
   • Work is published in MMSys 2024.
7. Ruoyu Zhao (Tsinghua University).
   • Research Intern 2023. Neural-based Representation for Volumetric Video Compression.
   • Work is published in MMVE 2024.
8. Shixin Ji (Xi'an Jiaotong University).
   • NGNE 2023. Human Activity Recognition In-the-Wild Using Wearable Devices.

• [2024/05] - Invited talk at Sichuan University, Sichuan.
• [2024/04] - Invited talk at Université de Toulouse, Toulouse INP-ENSEEIHT, IRIT, France.
• [2023/12] - Invited talk at National Tsing Hua University, Taiwan.
• [2022/11] - Student Volunteer, The 21st IEEE International Symposium on Mixed and Augmented Reality, 2022.

• ACM Multimedia (MM) 2024.
• Medical Image Analysis (MIA).
• IEEE Transactions on Cognitive and Developmental Systems (TCDS).
• ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM).