Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  introduce a variational framework towards automatic 3D building reconstruction from optical and Lidar data. Multiple 3D competing building priors are considered under a recognition-driven way. These models, under a certain hierarchical representation, describe the space of solutions and under a fruitful synergy with an inferential procedure recover the observed scene's geometry. Our formulation allows the cue with the higher spatial resolution to constrain properly the boundaries detection procedure ensuring, in this way, optimal results in terms of accuracy. Such an integrated approach is defined in a variational context, solves segmentation in both spaces, addresses fusion in a natural manner and allows multiple competing priors to determine the pose and 3D geometry from the observed data. Very promising experimental results demonstrate the potentials of our approach.
['Konstantinos Karantzalos', 'Nikos Paragios']
I am writing a research paper on a idea Variational model-based 3d building extraction from remote sensing data
[{"from": "human", "value": "I am writing a research paper on a idea Variational model-based 3d building extraction from remote sensing data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  introduce a variational framework towards automatic 3D building reconstruction from optical and Lidar data. Multiple 3D competing building priors are considered under a recognition-driven way. These models, under a certain hierarchical representation, describe the space of solutions and under a fruitful synergy with an inferential procedure recover the observed scene's geometry. Our formulation allows the cue with the higher spatial resolution to constrain properly the boundaries detection procedure ensuring, in this way, optimal results in terms of accuracy. Such an integrated approach is defined in a variational context, solves segmentation in both spaces, addresses fusion in a natural manner and allows multiple competing priors to determine the pose and 3D geometry from the observed data. Very promising experimental results demonstrate the potentials of our approach."}]