Indoor Instance Segmentation

Indoor instance segmentation aims to identify and delineate individual objects within 3D indoor scenes from point cloud or image data, enabling detailed scene understanding. Current research emphasizes improving accuracy and efficiency, focusing on novel network architectures like one-stage methods for faster inference and self-attention mechanisms to capture global context, as well as addressing challenges such as open-world scenarios and limited labeled data through techniques like self-supervised learning and domain adaptation. These advancements are crucial for applications in robotics, autonomous navigation, and digital twin creation, where precise object recognition and localization are essential.