季广达 Guangda Ji

I am a PhD student of GrUVi Lab at Simon Fraser University under the supervision of Prof. Hao Zhang. In general, I am interesed in computer graphics, geometric-modeling and visual problems.

Before that, I obtained my master degree in Data Science at ETH Zürich. I worked with Prof. Hermann Blum and Prof. Francis Engelmann at the CVG on video and 3D scene understanding. Previously, I obtained my Bachelor's Degree in Phycis from Peking University where I closely worked with Prof. Zhanxing Zhu.

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headshot
Research
Master Thesis: Moving and Deformable Object Detection
Guangda Ji.
code | thesis pdf
Master thesis supervised by Hermann Blum and Francis Engelmann.

The goal is to detect moving object relative to the static background and possibly the deforming objects, from purely RGB videos. See here for a short video demo.
ARKit LabelMaker: A New Scale for Indoor 3D Scene Understanding
Guangda Ji, Silvan Weder, Francis Engelmann, Marc Pollefeys, Hermann Blum.
CVPR, 2025
website | code | paper pdf | arxiv | dataset | ScanNet and ScanNet200 benchmark

We apply LabelMaker automatic annotation pipeline to ARKitScenes dataset and construct a large 3D indoor semantic segmentaiton dataset. Using this dataset and Point Transformer V3, we achieve near state-of-the-art performance on ScanNet and ScanNet200 benchmark.
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
Guangda Ji, Zhanxing Zhu.
Conference on Neural Information Processing Systems (NeurIPS), 2020
paper pdf | arxiv | poster | slides and recording

Theoretical analysis of knowledge distillation using neural tangent kernel (NTK).
Projects
3D Scene Understanding through CLIP Embedding and Instance Segmentation Fusion
Guangda Ji.
Semester thesis in 2023 supervised by Silvan Weder.
report pdf | codebase

In this project, I proposed a pipeline that fuses 2D instance segmentation into 3D. It first oversegments the 3D scene into pieces and merges them with a graph-connecting algorithm. This pipeline is designed to solve the route dependency issue of PannopticFusion.

Adaptive Mechanism Design in Sequential Social Dilemma
Guangda Ji, Minxuan Qin, Xuan'ang Lei.
Course team project of Foundations of Reinforcement Learning, 2023
report pdf | codebase

This project aims to apply Adaptive Mechanism Design (AMD), a multi-agent reinforcement learning algorithm to two complex Sequential Social Dilemma environment, Wolfpack and Gathering. We implement AMD in ray.rllib and the open source realization of Wolfpack and gathering.


Source code of this website. Template adapted from this awesome website