Abstract-We present an algorithm for learning 3D object models from partial object observations. The input to our algorithm is a sequence of 3D laser range scans. Models learned from the objects are represented as point clouds. Our approach can deal with partial views and it can robustly learn accurate models from complex scenes. It is based on an iterative matching procedure which attempts to recursively merge similar models. The alignment between models is determined using a novel scan registration procedure based on range images. The decision about which models to merge is performed by spectral clustering of a similarity matrix whose entries represent the consistency between different models. Index Terms-object detection, model learning , range images I. INTRODUCTION An essential ability for a system which interacts with unstructured environments is to learn models of the surrounding objects. Whereas many researchers focused on the detection of known object models in a 3D scene, relatively little work has been done on the learning of those models. In this paper, we present an approach for unsupervised learning of object models from a set of 3D scans. One special property of our algorithm is that it operates with partial views because in 3D laser range scans typically only a part of the object is visible. To get a complete model of an object, the robot therefore either needs to travel around it or needs to combine several views of identical objects in the current scene. Our approach can be applied to a sequence of 3D scans not necessarily acquired in the same environment. If multiple instances of an object are visible in a scene from different perpsectives, our approach can construct a model even from a single scan, by merging the different individual views. Such a model contains all available structrual information of an object. Figure 1 shows a motivating example of our algorithm. Our approach proceeds in an iterative fashion by recursively merging partial point clouds representing partial views of the objects. Pairs of models are aligned by means of a novel registration algorithm based on range images. The similarity between different models is evaluated by a scoring function specifically designed to cope with incomplete views. The selection of the models to be joined is calculated according to a spectral clustering of the similarity matrix. The remainder of this document is organized as follows. We first discuss the related literature on object model learning from 3D scans. Subsequently, we present our approach. Then, we show the effectiveness of the approach with real world experiments.
Ruhnke et al. REF proposed an approach to reconstructing full 3D models of objects by registering several partial views.
Unsupervised learning of 3D object models from partial views
2009 IEEE International Conference on Robotics and Automation
2009 IEEE International Conference on Robotics and Automation
Computer Science