Unsupervised 3D category discovery and point labeling from a large urban environment

The building of an object-level knowledge base is the foundation of a new methodology for many perception tasks in artificial intelligence, and is an area that has received increasing attention in recent years. In this paper, we propose, for the first time, to mine category shape patterns directly from a large urban environment, thus constructing a category structure base. Conventionally, category patterns are learned from a large collection of object samples, but automatic object collection requires prior knowledge of category structures. To solve this chicken-and-egg problem, we learn shape patterns from raw segmentations, and then refine these segmentations based on the pattern knowledge. In the process, we solve two challenging problems of knowledge mining. First, as some categories have large intra-category structure variations, we design an entropy-based method to determine the structure variation for each category, in order to establish the correct range of sample collection. Second, because incorrect segmentation is unavoidable without prior knowledge, we propose a novel unsupervised method that uses a pattern competition strategy to identify and subtract shape patterns formed by incorrectly segmented objects. This ensures that shape patterns are meaningful at the object level. Experimental results demonstrated the effectiveness of the proposed method for category structure mining in a large urban environment.