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This paper addresses the problem of recognizing free-form 3D objects in point clouds. Compared to traditional approaches based on point descriptors, which depend on local information around points, we propose a novel method that creates a global model description based on oriented point pair features and matches that model locally using a fast voting(More)
Object detection and localization is a crucial step for inspection and manipulation tasks in robotic and industrial applications. We present an object detection and lo-calization scheme for 3D objects that combines intensity and depth data. A novel multimodal, scale-and rotation-invariant feature is used to simultaneously describe the ob-ject's silhouette(More)
We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlu-sion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach(More)
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