Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present in this paper an interactive approach for semantically modeling indoor environments given only a singleindoor image as input, without requiring access to the scene or using any additional measurements like the RGBD cameras. Our key insight is that, although depth estimation from a single image is notoriously difficult,  can conveniently obtain a relatively accurate normal map, which essentially conveys a great deal of scene geometry. This enables us to model each object in a data-driven manner by representing the object as a normal-based graph and retrieving a similar model from the database by graph matching.  hypothesize a set of sparse surfaceorientations for the image, and further refine them in an intuitive and straightforward manner. With a small amount of simple user interaction, our approach is able to generate a plausible model of the scene. To verify the effectiveness of our proposed method,  show the modeling results on a variety of indoor images.
['Mingming Liu', 'Yanwen Guo', 'Jun Wang']
I am writing a research paper on a idea Normal Guided Data-Driven Semantic Modeling from a Single Indoor Image
[{"from": "human", "value": "I am writing a research paper on a idea Normal Guided Data-Driven Semantic Modeling from a Single Indoor Image"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present in this paper an interactive approach for semantically modeling indoor environments given only a singleindoor image as input, without requiring access to the scene or using any additional measurements like the RGBD cameras. Our key insight is that, although depth estimation from a single image is notoriously difficult,  can conveniently obtain a relatively accurate normal map, which essentially conveys a great deal of scene geometry. This enables us to model each object in a data-driven manner by representing the object as a normal-based graph and retrieving a similar model from the database by graph matching.  hypothesize a set of sparse surfaceorientations for the image, and further refine them in an intuitive and straightforward manner. With a small amount of simple user interaction, our approach is able to generate a plausible model of the scene. To verify the effectiveness of our proposed method,  show the modeling results on a variety of indoor images."}]