Abstract: Systems and methods for processing a pair of 2-D images are described. In one example, a stereoscopic set of images is converted into a collection of regions that represent individual 3-D objects in the pair of images. In one embodiment, the system recovers the 3-D point P for each point p that appears in both images. It estimates the 3-D orientation of the floor plane, and the image capture planes and their height from the floor. The system then identifies the collection B of points P that do not represent points on the floor and generates a projection C of B onto a plane parallel to the floor. It blurs the projection C and identifies peaks in the blurred image, then fits symmetric figures to the points in C around the identified peaks. The system projects the 3-D figures associated with the symmetric figures back onto the 2-D images.

Abstract: Systems and methods for processing a pair of 2-D images are described. In one example, a stereoscopic set of images is converted into a collection of regions that represent individual 3-D objects in the pair of images. In one embodiment, the system recovers the 3-D point P for each point p that appears in both images. It estimates the 3-D orientation of the floor plane, and the image capture planes and their height from the floor. The system then identifies the collection B of points P that do not represent points on the floor and generates a projection C of B onto a plane parallel to the floor. It blurs the projection C and identifies peaks in the blurred image, then fits symmetric figures to the points in C around the identified peaks. The system projects the 3-D figures associated with the symmetric figures back onto the 2-D images.

Abstract: Systems and methods for processing a pair of 2-D images are described. In one example, a stereoscopic set of images is converted into a collection of regions that represent individual 3-D objects in the pair of images. In one embodiment, the system recovers the 3-D point P for each point p that appears in both images. It estimates the 3-D orientation of the floor plane, and the image capture planes and their height from the floor. The system then identifies the collection B of points P that do not represent points on the floor and generates a projection C of B onto a plane parallel to the floor. It blurs the projection C and identifies peaks in the blurred image, then fits symmetric figures to the points in C around the identified peaks. The system projects the 3-D figures associated with the symmetric figures back onto the 2-D images.

Abstract: Systems and methods for processing a pair of 2-D images are described. In one example, a stereoscopic set of images is converted into a collection of regions that represent individual 3-D objects in the pair of images. In one embodiment, the system recovers the 3-D point P for each point p that appears in both images. It estimates the 3-D orientation of the floor plane, and the image capture planes and their height from the floor. The system then identifies the collection B of points P that do not represent points on the floor and generates a projection C of B onto a plane parallel to the floor. It blurs the projection C and identifies peaks in the blurred image, then fits symmetric figures to the points in C around the identified peaks. The system projects the 3-D figures associated with the symmetric figures back onto the 2-D images.

Abstract: A system processes 2D images of 2D or 3D objects, creating a model of the object that is consistent with the image and as veridical as the perception of the 2D image by humans. Vertices of the object that are hidden in the image are recovered by using planarity and symmetry constraints. The 3D shape is recovered by maximizing 3D compactness of the recovered object and minimizing its surface area. In some embodiments, these two criteria are weighted by using the geometric mean.

Abstract: A system processes 2D images of 2D or 3D objects, creating a model of the object that is consistent with the image and as veridical as the perception of the 2D image by humans. Vertices of the object that are hidden in the image are recovered by using planarity and symmetry constraints. The 3D shape is recovered by maximizing 3D compactness of the recovered object and minimizing its surface area. In some embodiments, these two criteria are weighted by using the geometric mean.