Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Standard approaches to stereo correspondence have difficulty when scene structure does not lie in or near the frontal parallel plane, in part because an orientation disparity as ll as a positional disparity is introduced.  propose a correspondence algorithm based on differential geometry, that takes explicit advantage of both disparities. The algorithm relates the 2D differential structure (position, tangent, and curvature) of curves in the left and right images to the Frenet approximation of the (3D) space curve. A compatibility function is defined via transport of the Frenet frames, and they are matched by relaxing this compatibility function on overlapping neighborhoods along the curve. The remaining false matches are concurrently eliminated by a model of "near" and "far" neurons derived from neurobiology. Examples on scenes with complex 3D structures are provided.
['Gang Li', 'Steven W. Zucker']
I am writing a research paper on a idea Contextual Inference in Contour-Based Stereo Correspondence
[{"from": "human", "value": "I am writing a research paper on a idea Contextual Inference in Contour-Based Stereo Correspondence"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Standard approaches to stereo correspondence have difficulty when scene structure does not lie in or near the frontal parallel plane, in part because an orientation disparity as ll as a positional disparity is introduced.  propose a correspondence algorithm based on differential geometry, that takes explicit advantage of both disparities. The algorithm relates the 2D differential structure (position, tangent, and curvature) of curves in the left and right images to the Frenet approximation of the (3D) space curve. A compatibility function is defined via transport of the Frenet frames, and they are matched by relaxing this compatibility function on overlapping neighborhoods along the curve. The remaining false matches are concurrently eliminated by a model of \"near\" and \"far\" neurons derived from neurobiology. Examples on scenes with complex 3D structures are provided."}]