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Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as superpix-els, is a widely used preprocessing step in segmentation algorithms. Superpixel methods reduce the number of regions that must be considered later by more computation-ally expensive algorithms, with a minimal loss of information. Nevertheless, as some(More)
— The idea that connected convex surfaces, separated by concave boundaries, play an important role for the perception of objects and their decomposition into parts has been discussed for a long time. Based on this idea, we present a new bottom-up approach for the segmentation of 3D point clouds into object parts. The algorithm approximates a scene using an(More)
The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated data-sets. As an alternative(More)
Today most recognition pipelines are trained at an off-line stage, providing systems with pre-segmented images and predefined objects, or at an on-line stage, which requires a human supervisor to tediously control the learning. Self-Supervised on-line training of recognition pipelines without human intervention is a highly desirable goal, as it allows(More)
In this paper we propose a novel spatially stratified sampling technique for evaluating the likelihood function in particle filters. In particular, we show that in the case where the measurement function uses spatial correspondence, we can greatly reduce computational cost by exploiting spatial structure to avoid redundant computations. We present results(More)
While humans can easily separate unknown objects into meaningful parts, recent segmentation methods can only achieve similar partitionings by training on human-annotated ground-truth data. Here we introduce a bottom-up method for segmenting 3D point clouds into functional parts which does not require supervision and achieves equally good results. Our method(More)
—Object recognition plays an important role in robotics, since objects/tools first have to be identified in the scene before they can be manipulated/used. The performance of object recognition largely depends on the training dataset. Usually such training sets are gathered manually by a human operator, a tedious procedure, which ultimately limits the size(More)