Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Augmented Reality (AR) applications, high quality relates to an accurate augmentation of virtual objects in the real scene. This can be accomplished only if the position of the observer is accurately known. This boils down to solving image-based location problem by an accurate camera pose (relative position and orientation) estimation, when a stereo or multiple camera setup is used. Consider a relevant application scenario as in a movie production set, where the director is able to preview a scene as an integrated view of the real scene augmented with animated 3D models. The main camera shoots the scene, where as secondary stereo camera pair is used for image registration and localization. The director can view the integrated preview from any viewpoint perfectly, as long as the camera pose estimation is accurate.   Moreover, in the case of a markerless AR system, the challenge for camera pose estimation, is strongly influenced by the precision of detected feature correspondences beten the images. Unfortunately, several of the state-of-art feature extractors (detectors and descriptors) cannot guarantee a consistent accuracy of camera pose estimation, especially at varied camera baselines (viewpoints). As a consequence, the precise augmentation of objects, as desired in an AR application, is compromised. Hence, it becomes necessary to understand the magnitude of this error in relation to the camera baseline depending on the chosen feature extractors.   , therefore, assess the quality of the position and the orientation of 3D reconstruction by evaluating 26 feature extractor combinations over 50 different camera baselines. To be directly relevant for AR applications,  evaluate by measuring the reconstruction error in 3D space, instead of re-projection error in 2D space. After the experiment,  have found the SIFT and KAZE feature extractors to be highly accurate and more robust to large camera baselines than others. Importantly, as a result of our study,  provide a recommendation for system builders to help them make a better choice of the feature extractor and/or the camera density required for their application.
['Deepak Dwarakanath', 'Carsten Griwodz', 'Pål Halvorsen']
I am writing a research paper on a idea Robustness of 3D point positions to camera baselines in markerless AR systems
[{"from": "human", "value": "I am writing a research paper on a idea Robustness of 3D point positions to camera baselines in markerless AR systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Augmented Reality (AR) applications, high quality relates to an accurate augmentation of virtual objects in the real scene. This can be accomplished only if the position of the observer is accurately known. This boils down to solving image-based location problem by an accurate camera pose (relative position and orientation) estimation, when a stereo or multiple camera setup is used. Consider a relevant application scenario as in a movie production set, where the director is able to preview a scene as an integrated view of the real scene augmented with animated 3D models. The main camera shoots the scene, where as secondary stereo camera pair is used for image registration and localization. The director can view the integrated preview from any viewpoint perfectly, as long as the camera pose estimation is accurate.   Moreover, in the case of a markerless AR system, the challenge for camera pose estimation, is strongly influenced by the precision of detected feature correspondences beten the images. Unfortunately, several of the state-of-art feature extractors (detectors and descriptors) cannot guarantee a consistent accuracy of camera pose estimation, especially at varied camera baselines (viewpoints). As a consequence, the precise augmentation of objects, as desired in an AR application, is compromised. Hence, it becomes necessary to understand the magnitude of this error in relation to the camera baseline depending on the chosen feature extractors.   , therefore, assess the quality of the position and the orientation of 3D reconstruction by evaluating 26 feature extractor combinations over 50 different camera baselines. To be directly relevant for AR applications,  evaluate by measuring the reconstruction error in 3D space, instead of re-projection error in 2D space. After the experiment,  have found the SIFT and KAZE feature extractors to be highly accurate and more robust to large camera baselines than others. Importantly, as a result of our study,  provide a recommendation for system builders to help them make a better choice of the feature extractor and/or the camera density required for their application."}]