Detection of 3D objects in cluttered scenes using hierarchical eigenspace

Abstract

This paper proposes a novel method to detect three-dimensional objects in arbitrary poses and sizes from a complex image and to simultaneously measure their poses and sizes using appearance matching. In the learning stage, for a sample object to be learned, a set of images is obtained by varying pose and size. This large image set is compactly represented by a manifold in compressed subspace spanned by eigenvectors of the image set. This representation is called the parametric eigenspace representation. In the object detection stage, a partial region in an input image is projected to the eigenspace, and the location of the projection relative to the manifold determines whether this region belongs to the object, and what its pose is in the scene. This process is sequentially applied to the entire image at different resolutions. Experimental results show that this method accurately detects the target objects.