DEEP CAMERA POSE REGRESSION USING MOTION VECTORS

A deep learning based camera pose regression framework is presented in this paper. The major objectives of the proposed method are twofold: enhancing the intra-scene pose regression accuracy and improving the inter-scene inference capability. Unlike other pose regression networks, the proposed framework adopts motion vectors as its input tensor, rather than directly taking the pixel intensities. Such concept is developed from two fundamental facts: the motion vectors are strongly associated with pose transition, and they are less relevant to scene-specific visual cue. Experimental results show that the proposed framework can achieve better performance in terms of intra-scene regression accuracy and inter-scene network inference.