Planarity Sensible Over Segmentation

Planarity-sensible over-segmentation is a technique used in image and point cloud segmentation that intentionally creates an initial over-segmented representation of the data, prioritizing the identification of planar regions or distinct clusters. Research focuses on improving the efficiency and accuracy of this initial over-segmentation step, often employing deep learning models like Segment Anything Model (SAM) variants and graph convolutional networks, and then refining the segmentation through merging or other post-processing techniques. This approach is particularly valuable in scenarios with complex or noisy data, such as medical images and 3D urban scenes, where it enhances the accuracy and robustness of subsequent semantic segmentation tasks, leading to improved performance in applications ranging from robotic mapping to medical image analysis.