Featureless 2D-3D Pose Estimation by Minimising an Illumination-Invariant Loss (1011.1035v1)

Abstract: The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision ranging from robotic vision to image analysis. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods: It does neither require prior training nor learning, nor knowledge of the camera parameters, nor explicit point correspondences or matching features between image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object, and works on a single static image from a given view, and under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection are presented.