Scene Structure
Scene structure research focuses on understanding and representing the spatial arrangement and relationships within visual scenes, aiming to enable more robust and intelligent computer vision systems. Current efforts concentrate on developing novel 3D scene reconstruction methods using techniques like Gaussian splatting, diffusion models, and neural radiance fields, often incorporating language guidance for enhanced control and semantic understanding. These advancements are crucial for applications ranging from autonomous navigation and augmented reality to video anomaly detection and generating realistic 3D scenes from limited input, impacting various fields including robotics, entertainment, and accessibility technologies.
Papers
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, Dani Lischinski
POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference
Zhiwen Fan, Panwang Pan, Peihao Wang, Yifan Jiang, Dejia Xu, Hanwen Jiang, Zhangyang Wang
An Impartial Transformer for Story Visualization
Nikolaos Tsakas, Maria Lymperaiou, Giorgos Filandrianos, Giorgos Stamou
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht, David Raba, Julian Jordan, Daniel Frank, Steffen Staab, Klaus Dietmayer