Qijian Tian

I'm a third-year Ph.D. student in Computer Science at Shanghai Jiao Tong University (SJTU), advised by Prof. Lizhuang Ma in Digital Media & Computer Vision Laboratory (DMCV). I also receive supervision from Dr. Xin Tan, who is based at East China Normal University (ECNU).

Prior to starting my PhD, I received my Bachelor's degree in Computer Science from Beihang University (BUAA). I also worked as an intern at Baidu .

Email / Scholar / Github

Research

My research interests involve computer vision and deep learning.
I am currently interested in MLLM and 3D vision, including spatial reasoning, 3D reconstruction, and 3D gaussian splatting.
I have previously conducted some work in 2D scene understanding, especially in scene parsing.

	DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input Qijian Tian, Xin Tan, Yuan Xie, Lizhuang Ma AAAI, 2025 project page / arXiv A feed-forward Gaussian Splatting model that reconstructs driving scenes from flexible sparse surround-view input.
	FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views Qijian Tian, Xin Tan, Jiayu Ying, Xuhong Wang, Yuan Xie, Lizhuang Ma, arXiv, 2025 project page / arXiv A feed-forward network that effectively reconstructs language-embedded 3D Gaussians in a single feed-forward pass from uncalibrated and unposed images, supporting both sparse and dense views.
	DANIM: Domain Adaptation Network with Intermediate Domain Masking for Night-time Scene Parsing Qijian Tian, Sen Wang, Ran Yi, Zufeng Zhang, Bin Sheng, Xin Tan, Lizhuang Ma Pattern Recognition, 2025 A novel domain adaptation network for night-time scene parsing that bridges the day-night domain gap using an intermediate domain.
	Generalized Category Discovery in Semantic Segmentation Zhengyuan Peng, Qijian Tian, JianQing Xu, Yizhang Jin, Xuequan Lu, Xin Tan, Yuan Xie, Lizhuang Ma arXiv, 2023 arXiv A novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS). Given prior knowledge from a labeled set of base classes, our method aims to segment unlabeled images that contain pixels of the base class or novel class.

This homepage's source code is from Jon Barron's website.