In the recent years, we experienced the proliferation of sensors for retrieving depth information on a scene, such as LIDAR or RGBD sensors (Kinect). However, it is still a challenge to identify the meaning of a specific point cloud to recognize the underlying object. Here, we wonder if it is possible to define a global feature for an object that is robust to noise, sampling and occlusion. We propose a local measure based on curvature. We called it Principal Curvatures because rather than using the Gaussian curvature we keep the
information of the two principal curvatures. In our approach, this local information is then aggregated as histograms that are compared with a Chi-2 metric. Results show the robustness of the method particularly when only few points are available. This means that our approach can be very suitable to match objects even with a limited resolution and possible occlusions. It could be particularly adapted to recognize objects with LIDAR inputs.