Object Recognition Methods in a Built Environment

Recognition of an object from a point cloud, image or video is an important task in computer vision which plays a crucial role in many real-world applications. The challenges involved in object recognition, aiming at locating object instances from a large number of predefined categories in collections (images, video or, model library), are multi-model, multi-pose, complicated background, occlusion, and depth variations. In the past few years numerous methods were developed to tackle these challenges and have reported remarkable progress for 3D objects. However, suitable methods of object recognition are needed to achieve added value in built environment. Suitable acquisition methods are also necessary to compensate the impact of darkness, dirt, and occlusion. This chapter provides a comprehensive overview of the recent advances in 3D object recognition of indoor objects using Convolutional Neural Networks (CNN). Methodology for object recognition, approaches for point cloud generation, and test bases are presented. The comparison of main recognition methods based on methods of geometric shape descriptor and supervised learning and their strengths and weakness are also included. The focus lies on the specific requirements and constrains in an industrial environment like tight assembly, light, dirt, occlusion, or incomplete data sets. Finally, a recommendation for use of existing CNN framework for implementation of an automatic object recognition procedure is given.