3D Semantic

3D semantic understanding aims to computationally represent and interpret the meaning of three-dimensional scenes, going beyond simple geometric reconstruction to encompass object recognition, segmentation, and scene understanding. Current research focuses on developing robust methods for 3D semantic segmentation using various data sources (e.g., point clouds, images, depth maps) and architectures (e.g., convolutional neural networks, transformers, radiance fields), often incorporating self-supervised learning and multi-modal fusion techniques to improve accuracy and efficiency. This field is crucial for advancing applications in robotics, autonomous driving, and virtual/augmented reality, where accurate and detailed 3D scene understanding is essential for effective interaction and decision-making.