3D Semantic Map

3D semantic maps represent environments as three-dimensional point clouds enriched with semantic labels, aiming to provide robots and autonomous systems with a comprehensive understanding of their surroundings. Current research focuses on building these maps efficiently and robustly using techniques like convolutional neural networks for semantic segmentation, self-supervised learning for instance identification, and integration with large language models for natural language instruction processing and planning. This technology is crucial for advancing robotics, autonomous driving, and augmented/virtual reality applications by enabling more sophisticated scene understanding and interaction capabilities.