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.