Kevin Qu

I am a final-year Master's student in Robotics, Systems and Control at ETH Zürich. Currently, I am a Visiting Student Researcher at Stanford University in the Gradient Spaces Lab, advised by Prof. Iro Armeni.

During my Master's, I was a research intern at the Microsoft Spatial AI Lab under Prof. Marc Pollefeys, where I focused on spatial video understanding with Vision-Language Models (VLMs). I was also a research assistant at Prof. Konrad Schindler's PRS Lab at ETH, working on diffusion models for dense prediction tasks. Before that, I obtained a Bachelor's in Electrical Engineering from the Technical University of Munich. During this time, I was a research intern at the University of Victoria under Prof. Lin Cai, working on communication networks for autonomous driving, and spent a semester abroad at the University of Edinburgh.

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headshot
Research

My research interests lie in computer vision and machine learning, with a focus on 3D scene understanding and generative models.

(* denotes equal contribution)

Loc3R-VLM: Language-based Localization and 3D Reasoning with Vision-Language Models
Kevin Qu, Haozhe Qi, Mihai Dusmanu, Mahdi Rad, Rui Wang, Marc Pollefeys
arXiv 2026
Paper | Project Page

Equipping 2D Vision-Language Models with 3D spatial understanding capabilities. Inspired by human cognition, we guide the model to learn global scene structure and local viewpoint awareness directly from monocular video.

Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis
Bingxin Ke*, Kevin Qu*, Tianfu Wang*, Nando Metzger*, Shengyu Huang, Bo Li, Anton Obukhov, Konrad Schindler
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2025
Paper | Project Page | Code | Demo

Repurposing text-to-image diffusion models for a range of dense prediction tasks, including monocular depth estimation, surface normal prediction, and intrinsic image decomposition.

Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Massimiliano Viola, Kevin Qu, Nando Metzger, Bingxin Ke, Alexander Becker, Konrad Schindler, Anton Obukhov
International Conference on Computer Vision (ICCV) 2025
Paper | Project Page | Code | Demo

Training-free framework for zero-shot depth completion. We use Marigold as an off-the-shelf monocular depth estimator and guide its diffusion process with sparse depth observations.


Last update March 2026. Thanks for the website template.