Depth-Enhanced Hough Forests for Object-Class Detection and Continuous Pose Estimation

Ishrat Badami, Jörg Stückler, Sven Behnke
In 3rd Workshop on Semantic Perception, Mapping and Exploration (SPME), ICRA, 2013

Much work on the detection and pose estimation of objects in the robotics context focused on object instances. We propose a novel approach that detects object classes and finds the pose of the detected objects in RGB-D images. Our method is based on Hough forests, a variant of random decision and regression trees that categorize pixels and vote for 3D object position and orientation. It makes efficient use of dense depth for scale-invariant detection and pose estimation. We propose an effective way to train our method for arbitrary scenes that are rendered from training data in a turn-table setup. We evaluate our approach on publicly available RGB-D object recognition benchmark datasets and demonstrate stateof-the-art performance in varying background and view poses, clutter, and occlusions.



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