Indoor Dataset

Indoor datasets are crucial for training and evaluating algorithms for 3D scene understanding in robotics and augmented reality applications. Current research focuses on creating larger, more diverse datasets encompassing dynamic scenes and various object types, along with developing robust models—including transformer-based architectures and hierarchical reinforcement learning—to handle the complexities of indoor environments. These advancements are driving progress in tasks like 3D object detection, semantic segmentation, and robot navigation, ultimately improving the capabilities of autonomous systems in indoor settings. The development of standardized benchmarks and evaluation metrics is also a key area of focus, ensuring reproducibility and facilitating fair comparison of different approaches.