Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A common limitation of many techniques for 3D reconstruction from multiple perspective views is the poor quality of the results near the object boundaries. The interpolation process applied to "unstructured" 3D data ("clouds" of non-connected 3D points) plays a crucial role in the global quality of the 3D reconstruction.  present a method for interpolating unstructured 3D data, which is able to perform a segmentation of such data into different data sets that correspond to different objects. The algorithm is also able to perform an accurate localization of the boundaries of the objects. The method is based on an iterative optimization algorithm. As a first step, a set of surfaces and boundary curves are generated for the various objects. Then, the edges of the original images are used for refining such boundaries as best as possible. Experimental results with real data are presented for proving the effectiveness of the proposed algorithm.
['Federico Pedersini', 'Augusto Sarti', 'Stefano Tubaro']
I am writing a research paper on a idea Combined surface interpolation and object segmentation for automatic 3D scene reconstruction
[{"from": "human", "value": "I am writing a research paper on a idea Combined surface interpolation and object segmentation for automatic 3D scene reconstruction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A common limitation of many techniques for 3D reconstruction from multiple perspective views is the poor quality of the results near the object boundaries. The interpolation process applied to \"unstructured\" 3D data (\"clouds\" of non-connected 3D points) plays a crucial role in the global quality of the 3D reconstruction.  present a method for interpolating unstructured 3D data, which is able to perform a segmentation of such data into different data sets that correspond to different objects. The algorithm is also able to perform an accurate localization of the boundaries of the objects. The method is based on an iterative optimization algorithm. As a first step, a set of surfaces and boundary curves are generated for the various objects. Then, the edges of the original images are used for refining such boundaries as best as possible. Experimental results with real data are presented for proving the effectiveness of the proposed algorithm."}]