Surface-Based Detection and 6-DoF Pose Estimation of 3-D Objects in Cluttered Scenes

In this paper, we propose a novel approach for both 3-D object detection and six-degree-of-freedom (6-DoF) pose estimation based on smooth 3-D surface segments and their visual signatures in cluttered scenes with objects partially occluded. It is an appearance-based approach that does not require precise geometric models of the objects. We introduce a robust and flexible strategy to build object models based on segmented smooth surfaces of RGB-D images. We next introduce a strategy to detect objects and estimate their 6-DoF poses from a single image by taking full advantage of the surface-based object models. Our approach can reconstruct objects in cluttered scenes from a single view, where different objects or multiple instances of the same object can be stacked together or physically attached and occlude one another. It detects objects and estimates their 6-DoF poses even if the objects have transparent regions. Experimental results and comparison with related work demonstrate the effectiveness of our approach.