Primitive Segmentation

Primitive segmentation focuses on identifying and delineating basic geometric shapes (lines, circles, planes, etc.) within complex data, such as images or point clouds, to simplify representation and analysis. Current research emphasizes developing robust and efficient algorithms, often leveraging deep learning architectures like transformers and neural radiance fields, to achieve accurate segmentation across diverse data types including remote sensing imagery, medical scans, and 3D models. This work has significant implications for various fields, enabling improved object recognition, scene understanding, and automated analysis in applications ranging from urban planning and medical diagnosis to 3D modeling and computer-aided design.