Sayan Deb Sarkar

I am a first year Computer Science MSc student at ETH Zurich, majoring in Visual Interactive Computing with a minor in Machine Learning. My research interests lie at the intersection of Computer Vision and Machine Learning, specifically in the areas of 3D scene understanding and pose estimation. Currently, I am working at the Computer Vision and Geometry Group of Prof. Marc Pollefeys with Iro Armeni and Ondrej Miksik on Multi-Modal scene alignment with 3D Scene Graphs.

Prior to starting my MSc, I gained 2.5 years of experience working as a Computer Vision Research Engineer at Mercedes-Benz R&D , on Intelligent Interior Camera Systems, for around a year, and as a Research Assistant for the other one and a half years with Prof. Vincent Lepetit at the Institute of Computer Graphics and Vision, TU Graz , funded by Qualcomm Inc. My work has been published multiple times in top-tier Vision conferences such as CVPR, ICCV and ECCV.

I am always looking for interesting research collaborations and ideas, please get in touch via email for any such opportunities. Also, if you're in or around Zurich, feel free to reach out, I am always up for a good cup of coffee!

CV / Google Scholar / Twitter / Github / LinkedIn

Recent News

Jul 2023 : SGAligner accepted to ICCV 2023, my first first-author submission!
Apr 2023 : We're organising the workshop CV4AEC @ CVPR 2023 , participate in the 2D and 3D challenges!
Oct 2022 : Started working at CVG on scene understanding
Sep 2022 : Moved to Zurich! started MSc at ETH
Jun 2022 : Keypoint Transformer accepted at CVPR 2022 as an Oral presentation
Mar 2022 : Got accepted to ETH Zurich and MILA Montreal for MSc CS!
May 2021 : Started at Mercedes-Benz R & D as a Computer Vision Research Engineer!

Research

	SGAligner : 3D Scene Alignment with Scene Graphs Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni arXiv / Project Page / Video / Code International Conference on Computer Vision (ICCV), 2023 We focus on the fundamental problem of aligning pairs of 3D scene graphs whose overlap can range from zero to partial and can contain arbitrary changes. We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios.
	Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation Shreyas Hampali, Sayan Deb Sarkar, Mahdi Rad, Vincent Lepetit Computer Vision and Pattern Recognition (CVPR), 2022 Oral arXiv / Project Page / Video / Code We propose an efficient network architecture for estimating pose of two hands and object during complex interaction. We also release the challenging H2O-3D dataset, which contains two hands interacting with YCB objects.
	Monte Carlo Scene Search for 3D Scene Understanding Shreyas Hampali, Sinisa Stekovic, Sayan Deb Sarkar, Chetan Srinivasa Kumar, Friedrich Fraundorfer, Vincent Lepetit Computer Vision and Pattern Recognition (CVPR), 2021 arXiv / Project Page / Video / Code We propose a Monte-Carlo Tree Search (MCTS) based analysis-by-synthesis method to recover complete scene (3D layout+objects) from a RGB-D scan of the environment. *Equal contribution
	General 3D Room Layout from a Single View by Render-and-Compare Sinisa Stekovic, Shreyas Hampali, Mahdi Rad, Sayan Deb Sarkar, Friedrich Fraundorfer, Vincent Lepetit European Conference on Computer Vision (ECCV), 2020 arXiv / Project Page / Video / Code We propose an analysis-by-synthesis method to estimate a 3D layout of the room - walls, floors, ceilings - from a single perspective view. The method recovers complex non-cubiod layouts by solving a constrained discrete optimization problem.

Course Projects

Ray Tracing
Computer Graphics Rendering Competition, Autumn Semester 2022

Implemented a ray tracer with functionalities such as advanced camera models, participating media, photon mapping, Disney BRDF, etc on the Nori framework.

Design and code from Jon Barron's website