3D Segmentation

3D segmentation aims to partition three-dimensional data, such as point clouds or volumetric images, into meaningful regions based on semantic or instance-level properties. Current research emphasizes efficient algorithms, often leveraging transformer architectures or adapting successful 2D models (like Segment Anything Model) to the 3D domain, with a focus on semi-supervised or weakly-supervised learning to reduce reliance on extensive manual annotation. This field is crucial for numerous applications, including medical image analysis, autonomous driving, and robotics, enabling more accurate and automated analysis of complex 3D scenes.