3D Semantic Scene

3D semantic scene understanding aims to create comprehensive, computer-interpretable models of real-world environments, capturing both the geometry and semantic labels of objects and spaces. Current research emphasizes improving the accuracy and efficiency of scene reconstruction from various data sources (LiDAR, RGB-D cameras, multi-view videos), often employing deep learning architectures like neural radiance fields (NeRFs) and transformers, along with techniques such as contrastive learning and multi-scan fusion to handle challenges like long distances and occlusions. This field is crucial for advancing robotics, autonomous navigation, augmented reality, and other applications requiring robust scene interpretation and interaction.