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Saliency detection and local feature extraction for 2D images have received extensive attention recently. In this paper, we propose saliency detection and feature extraction techniques for 3D visual data. Our algorithm directly works in 3D scale space and detects interesting regions in different scales. We then extract a local descriptor based on gradient location-orientation histogram which is invariant to scale and rotation of the 3D object. The proposed methodology has been tested on 3D synthetic and Magnetic Resonance Imaging (MRI) data sets. The performance of the algorithm is evaluated based on the repeatability of saliency detection and descriptor matching, after 3D transformation and in the presence of noise.