Computer Vision and Geometry Group

Open Opportunities

Lane Detection for Autonomous Driving with F1Tenth Car ETH Zurich Computer Vision and Geometry Group In this semester thesis, our goal is to enable an F1Tenth car, an autonomous vehicle at 1:10 scale of a Formula 1 car, to accurately detect its designated driving lane using RGB-D images captured by an onboard camera. Computer Vision, Intelligent Robotics Semester Project

Metric (Semi-)Monocular Depth Estimation ETH Zurich Computer Vision and Geometry Group The goal of the project is to augment existing monocular depth estimation models with measured sparse metric depth and fuse the information into accurate metric depth maps. Information, Computing and Communication Sciences Master Thesis

Object Pose Estimation using Line and Point Features ETH Zurich Computer Vision and Geometry Group We hope to push the state of the art on object pose estimation, especially for textureless objects, by using line features as well as point features. Information, Computing and Communication Sciences Master Thesis, Semester Project

Topological Object Search ETH Zurich Computer Vision and Geometry Group In this project, we want to break with the requirement of geometric sensing. We want to use only a camera. This has the advantage that it can work indoors and outdoors, has no problem with reflective surfaces etc. To explore and search without geometric sensing, we want to base the algorithm on 'directions of interest'. Wherever the robot goes and sees something interesting (e.g. an open door), it will just go there. Intelligent Robotics Master Thesis, Semester Project

Automatic Scene Graph Generation ETH Zurich Computer Vision and Geometry Group In this thesis, we want to create a tool that easily and automatically creates such scene graphs from a single iPad scan of the room, such that a robot can be deployed to the environment. We base this on LabelMaker, where we add instance segmentation, and separation of rooms and floors. Computer Vision Master Thesis, Semester Project

3D Reconstruction of Water in a Glass ETH Zurich Computer Vision and Geometry Group The project is about reconstructing a dynamic scene of water, glass, and an object thrown into the water. The input is images from 2-3 synchronized RGB cameras. The expected output is the 3D reconstruction of each frame, ideally optimized so that the motion is consistent. Computer Vision Master Thesis

GNSS/RTK-SLAM fusion for accurate positioning of geospatial data in Mixed Reality ETH Zurich Computer Vision and Geometry Group The main objective of the project is to increase the accuracy and usability of the Mixed Reality solution developed by V-Labs. The V-Labs team expects that the integration of a fusion algorithm based on Artificial Intelligence or an Unscented Kalman Filter (UKF) will be able to reach that goal. Information, Computing and Communication Sciences Master Thesis

Accurate SLAM for Human-Robot Teams ETH Zurich Computer Vision and Geometry Group We extend the lamar.ethz.ch benchmark to develop accurate SLAM methods that can co-register drones, legged robots, wheeled robots, smartphones, and mixed reality headsets based on visual SLAM. Computer Vision, Intelligent Robotics Bachelor Thesis, Master Thesis, Semester Project

Human-Robot Communication with Text Prompts and 3D Scene Graphs ETH Zurich Computer Vision and Geometry Group This project extends previous work [a] on calculating similarity scores between text prompts and 3D scene graphs representing environments. The current method identifies potential locations based on user descriptions, aiding human-agent communication, but is limited by its coarse localization and inability to refine estimates incrementally. This project aims to enhance the method by enabling it to return potential locations within a 3D map and incorporate additional user information to improve localization accuracy incrementally until a confident estimate is achieved. [a] Chen, J., Barath, D., Armeni, I., Pollefeys, M., & Blum, H. (2024). "Where am I?" Scene Retrieval with Language. ECCV 2024. Computer Vision Master Thesis, Semester Project

3D Surface Reconstruction from Sparse Viewpoints for Medical Education and Surgical Navigation ETH Zurich Computer Vision and Geometry Group Other organizations: Research in Orthopedic Computer Science In medical education and surgical navigation, achieving accurate multi-view 3D surface reconstruction from sparse viewpoints is a critical challenge. This Master's thesis addresses this problem by first computing normal and optionally reflectance maps for each viewpoint, and then fusing this data to obtain the geometry of the scene and, optionally, its reflectance. The research explores multiple techniques for normal map computation, including photometric stereo, data-driven methods, and stereo matching, either individually or in combination. The outcomes of this study aim to pave the way for the creation of highly realistic and accurate 3D models of surgical fields and anatomical structures. These models have the potential to significantly improve medical education by providing detailed and interactive representations for learning. Additionally, in the context of surgical navigation, these advancements can enhance the accuracy and effectiveness of surgical procedures. References: Yu, Zehao, Peng, Songyou, Niemeyer, Michael, Sattler, Torsten, Geiger, Andreas. MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction. NeurIPS 2022. Baptiste Brument and Robin Bruneau and Yvain Quéau and Jean Mélou and François Lauze and Jean-Denis Durou and Lilian Calvet. RNb-Neus: Reflectance and normal Based reconstruction with NeuS. CVPR 2024. Gwangbin Bae and Andrew J. Davison. Rethinking Inductive Biases for Surface Normal Estimation. CVPR 2024. Computer Vision Master Thesis