3d object detection using random forests

Abstract

Example embodiments allow for fast, efficient detection and pose estimation of objects based on point clouds, depth images/maps, or other depth information about a scene that may contain the objects. Embodiments include translating and rotating the depth image to bring individual points of the depth image to a standard orientation and location so as to improve performance when an object is near the periphery of the field of view. Some disclosed embodiments include applying a random forest to perform pose estimation. By using the decision trees or other fast methods, it can be advantageous to perform pose estimation a plurality of times prior to identifying whether a particular object is actually present in a scene. Prospective pose estimates can be combined with models of the objects in order to evaluate whether the object is present in the scene.