Interest point detection in depth images through scale-space surface analysis

Many perception problems in robotics such as object recognition, scene understanding, and mapping are tackled using scale-invariant interest points extracted from intensity images. Since interest points describe only local portions of objects and scenes, they offer robustness to clutter, occlusions, and intra-class variation. In this paper, we present an efficient approximate algorithm to extract surface normal interest points (SNIPs) in corners and blob-like surface regions from depth images. The interest points are detected on characteristic scales that indicate their spatial extent. Our method is able to cope with irregularly sampled, noisy measurements which are typical to depth imaging devices. It also offers a trade-off between computational speed and accuracy which allows our approach to be applicable in a wide range of problem sets. We evaluate our approach on depth images of basic geometric shapes, more complex objects, and indoor scenes.