Partial 3d Object Retrieval And Completeness Evaluation For Urban Street Scene

3D objects detected from real-world data are usually incomplete in different degrees. Objects with different degrees of incompleteness should be treated and processed separately. This paper proposes a framework for partial 3D object retrieval and completeness evaluation in an urban street scene based on mobile laser scanning (MLS) point cloud data. The framework consists of three parts. A deep learning method is first used to detect objects from 3D point cloud data. Then, for each detected object, the most similar object in the reference dataset, which contains complete objects, is obtained by a partial 3D shape retrieval method. Last, a completeness evaluation of the detected object is conducted by calculating the completeness index that reflects the integrity of the detected object, and a missing part prediction is given to guide further completion. The proposed framework is validated on the public dataset KITTI and our own point cloud dataset. The experiment includes 3D detection, the partial 3D shape retrieval, and the completeness evaluation. Results show the good performance of the object detection and partial shape retrieval, also a reasonable evaluation of objects completeness.