3D Object detection and viewpoint selection in sketch images using local patch-based Zernike moments

International audienceIn this paper we present a new approach to detect andrecognize 3D models in 2D storyboards which have beendrawn during the production process of animated cartoons.Our method is robust to occlusion, scale and rotation. Thelack of texture and color makes it difficult to extract localfeatures of the target object from the sketched storyboard.Therefore the existing approaches using local descriptorslike interest points can fail in such images. We proposea new framework which combines patch-based Zernike descriptorswith a method enforcing spatial constraints for exactlydetecting 3D models represented as a set of 2D viewsin the storyboards. Experimental results show that the proposedmethod can deal with partial object occlusion and issuitable for poorly textured objects

3D Object detection and viewpoint selection in sketch images using local patch-based Zernike moments

International audienceIn this paper we present a new approach to detect andrecognize 3D models in 2D storyboards which have beendrawn during the production process of animated cartoons.Our method is robust to occlusion, scale and rotation. Thelack of texture and color makes it difficult to extract localfeatures of the target object from the sketched storyboard.Therefore the existing approaches using local descriptorslike interest points can fail in such images. We proposea new framework which combines patch-based Zernike descriptorswith a method enforcing spatial constraints for exactlydetecting 3D models represented as a set of 2D viewsin the storyboards. Experimental results show that the proposedmethod can deal with partial object occlusion and issuitable for poorly textured objects