Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract   ,  address the problem of recovering 3D scene structure from omni-directional cylindrical panoramic images. In our application, the images are obtained from a single omni-directional camera mounted on top of a mobile robot. Depth can be estimated from a single pair of images by a stereo vision technique. Hover, depth cannot be estimated reliably in the robot’s heading direction. Moreover, depth estimates obtained from a single pair of images tend to be noisy. To overcome these problems  have developed a multi-baseline stereo vision method which uses more than two images obtained from multiple non-co-linear viewpoints. Depth data obtained from different pairs of images is fused in a probabilistic framework.  present our method and experimental results obtained using image data acquired by our robot. The results indicate that robust and reliable depth estimates can be obtained using the proposed method.
['Roland Bunschoten', 'Ben J. A. Kröse']
I am writing a research paper on a idea 3D scene reconstruction from cylindrical panoramic images
[{"from": "human", "value": "I am writing a research paper on a idea 3D scene reconstruction from cylindrical panoramic images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract   ,  address the problem of recovering 3D scene structure from omni-directional cylindrical panoramic images. In our application, the images are obtained from a single omni-directional camera mounted on top of a mobile robot. Depth can be estimated from a single pair of images by a stereo vision technique. Hover, depth cannot be estimated reliably in the robot\u2019s heading direction. Moreover, depth estimates obtained from a single pair of images tend to be noisy. To overcome these problems  have developed a multi-baseline stereo vision method which uses more than two images obtained from multiple non-co-linear viewpoints. Depth data obtained from different pairs of images is fused in a probabilistic framework.  present our method and experimental results obtained using image data acquired by our robot. The results indicate that robust and reliable depth estimates can be obtained using the proposed method."}]