A 3D Keypoint Detector based on Biologically Motivated Bottom-Up Saliency Map

We present a new method for the detection of 3D keypoints on point clouds and we perform benchmarking between each pair of 3D keypoint detector and 3D descriptor to evaluate their performance on object and category recognition. Our keypoint detector is inspired by the behavior and neural architecture of the primate visual system. The 3D keypoints are extracted based on a bottom-up 3D saliency map, that is, a map that encodes the saliency of objects in the visual environment. The saliency map is determined by computing conspicuity maps of the orientation, in-tensity and color information in a bottom-up and in a purely stimulus-driven manner. Finally, the focus of attention (or “keypoint location") is sequentially directed to the most salient points in this map. The main conclusions are: with a similar average number of keypoints, our 3D key-point detector outperforms the other 3D keypoint detectors evaluated i