Object recognition based on local features computed at multiple locations is robust to occlusions, strong viewpoint changes and object deformations. These features should be repeatable, precise and distinctive. We present an operator for repeatable feature detection on depth images (relative to 3D models) as well as 2D intensity images. The proposed detector is based on estimating the curviness saliency at multiple scales in each kind of image. We also propose quality measures that evaluate the repeatability of the features between depth and intensity images. The experiments show that the proposed detector outperforms both the most powerful, classical point detectors (e.g., SIFT) and edge detection techniques.
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