Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new approach for large-scale multi-view stereo matching, which is designed to operate on ultra high-resolution image sets and efficiently compute dense 3D point clouds.  show that, using a robust descriptor for matching purposes and high-resolution images,  can skip the computationally expensive steps that other algorithms require. As a result, our method has low memory requirements and low computational complexity while producing 3D point clouds containing virtually no outliers. This makes it exceedingly suitable for large-scale reconstruction. The core of our algorithm is the dense matching of image pairs using DAISY descriptors, implemented so as to eliminate redundancies and optimize memory access.  use a variety of challenging data sets to validate and compare our results against other algorithms.
['Engin Tola', 'Christoph Strecha', 'Pascal Fua']
I am writing a research paper on a idea Efficient large-scale multi-view stereo for ultra high-resolution image sets
[{"from": "human", "value": "I am writing a research paper on a idea Efficient large-scale multi-view stereo for ultra high-resolution image sets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a new approach for large-scale multi-view stereo matching, which is designed to operate on ultra high-resolution image sets and efficiently compute dense 3D point clouds.  show that, using a robust descriptor for matching purposes and high-resolution images,  can skip the computationally expensive steps that other algorithms require. As a result, our method has low memory requirements and low computational complexity while producing 3D point clouds containing virtually no outliers. This makes it exceedingly suitable for large-scale reconstruction. The core of our algorithm is the dense matching of image pairs using DAISY descriptors, implemented so as to eliminate redundancies and optimize memory access.  use a variety of challenging data sets to validate and compare our results against other algorithms."}]