Abstract-Recent years have seen advances in the estimation of full 6 degree-of-freedom object pose from a single 2D image. These advances have often been presented as a result of, or together with, a new local image descriptor. This paper examines how the performance for such a system varies with choice of local descriptor. This is done by comparing the performance of a full 6 degree-of-freedom pose estimation system for fourteen types of local descriptors. The evaluation is done on a database with photos of complex objects with simple and complex backgrounds and varying lighting conditions. From the experiments we can conclude that duplet features, that use pairs of interest points, improve pose estimation accuracy, and that affine covariant features do not work well in current pose estimation frameworks. The data sets and their ground truth is available on the web to allow future comparison with novel algorithms.
Point-of-interest detection is a way of reducing the amount of data that needs to be processed in a certain application and is widely used in 2D image analysis. In 2D image analysis, point-of-interest detection is usually related to extraction of local descriptors for object recognition, classification, registration or pose estimation. In analysis of range data however, some local descriptors have been published in the last decade or so, but most of them do not mention any kind of point-ofinterest detection. We here show how to use an extended Harris detector on range data and discuss variants of the Harris measure. All described variants of the Harris detector for 3D should also be usable in medical image analysis, but we focus on the range data case. We do present a performance evaluation of the described variants of the Harris detector on range data.
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