Register now After registration you will be able to apply for this opportunity online.
Gaussian Splatting Visual Odometry
Gaussian Splatting Visual Odometry
Keywords: Computer Vision, Visual Odometry
Recent works have shown that Gaussian Splatting (GS) is a compact and accurate map representation. Thanks to their properties GS maps are appealing for SLAM systems. However, recent works including GS maps in SLAM struggle with map-to-frame mapping. In this project, we will investigate the potential of GS maps in VO. The goal is to achieve robust map-to-frame tracking. We will benchmark our solution against feature-based and direct-based tracking baselines.
This project will be done in collaboration with Meta.
Recent works have shown that Gaussian Splatting (GS) is a compact and accurate map representation. Thanks to their properties GS maps are appealing for SLAM systems. However, recent works including GS maps in SLAM struggle with map-to-frame mapping. In this project, we will investigate the potential of GS maps in VO. The goal is to achieve robust map-to-frame tracking. We will benchmark our solution against feature-based and direct-based tracking baselines. This project will be done in collaboration with Meta.
The goal is to investigate the use of Gaussian splatting maps in visual-inertial systems. We look for students with strong programming (C++ preferred), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic backgrounds.
The goal is to investigate the use of Gaussian splatting maps in visual-inertial systems. We look for students with strong programming (C++ preferred), computer vision (ideally have taken Prof. Scaramuzza's class), and robotic backgrounds.