Holistic Scene Understanding

Holistic scene understanding aims to enable machines to comprehensively interpret visual scenes, going beyond simple object recognition to encompass semantic segmentation, depth estimation, and spatial relationships. Current research focuses on integrating diverse data modalities (RGB images, LiDAR point clouds, etc.) using deep learning architectures like transformers and incorporating techniques such as panoptic segmentation and scene graph generation to achieve more complete scene representations. This field is crucial for advancing robotics, autonomous driving, and other applications requiring robust environmental perception, with ongoing efforts concentrating on improving model efficiency, generalization capabilities, and the handling of complex, dynamic scenes.