Scene Representation
Scene representation in computer vision and robotics aims to create effective digital models of real-world environments for tasks like robot navigation, visual localization, and scene understanding. Current research focuses on developing efficient and accurate scene representations using various approaches, including neural radiance fields (NeRFs), Gaussian splatting, and graph neural networks, often incorporating semantic information and leveraging large language models for improved scene interpretation and interaction. These advancements are crucial for improving the capabilities of autonomous systems and enabling more sophisticated applications in robotics, augmented reality, and other fields requiring robust scene understanding.
Papers
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022
Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson
Recent Advances in Scene Image Representation and Classification
Chiranjibi Sitaula, Tej Bahadur Shahi, Faezeh Marzbanrad, Jagannath Aryal