Superquadrics-based object representation of complex scenes from range images

This paper investigates the superquadrics-based object representation of complex scenes from range images. The issues on how the recover-and-select algorithm is incorporated to handle complex scenes containing background and multiple occluded objects are addressed respectively. For images containing backgrounds, the raw image is first coarsely segmented using the scan-line grouping technique. An area threshold is then taken to remove the backgrounds while keeping all the objects. After this pre-segmentation, the recover-and-select algorithm is applied to recover superquadric (SQ) models. For images containing multiple occluded objects, a circle-view strategy is taken to recover complete SQ models from range images in multiple views. First, a view path is planned as a circle around the objects, on which images are taken approximately every 45 degrees. Next, SQ models are recovered from each single-view range image. Finally, the SQ models from multiple views are registered and integrated. These approaches are tested on synthetic range images. Experimental results show that accurate and complete SQ models are recovered from complex scenes using our strategies. Moreover, the approach handling background problems is insensitive to the pre-segmentation error.