Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate detection and localization of general 3D object classes by relating local scale-invariant features to a viewpoint invariant reference frame. This can generally be achieved by either a multi-view representation, where features and reference frame are modeled as a collection of distinct views, or by a viewpoint invariant representation, where features and reference frame are modeled independently of viewpoint.  compare multi-view and viewpoint invariant representations trained and tested on the same data, where the viewpoint invariant approach results in fer false positive detections and higher average precision.  present a new, iterative learning algorithm to determine an optimal viewpoint invariant reference frame from training images in a data-driven manner. The learned optimal reference frame is centrally located with respect to the 3D object class and to image features in a given view, thereby minimizing reference frame localization error as predicted by theory and maintaining a consistent geometrical interpretation with respect to the underlying object class. Modeling and detection based on the optimal reference frame improves detection performance for both multiview and viewpoint invariant representations. Experimentation is performed on the class of 3D faces, using the public color FERET database for training, the CMU profile database for testing and SIFT image features.
['Matthew Toews', 'Tal Arbel']
I am writing a research paper on a idea Detecting and Localizing 3D Object Classes using Viewpoint Invariant Reference Frames
[{"from": "human", "value": "I am writing a research paper on a idea Detecting and Localizing 3D Object Classes using Viewpoint Invariant Reference Frames"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate detection and localization of general 3D object classes by relating local scale-invariant features to a viewpoint invariant reference frame. This can generally be achieved by either a multi-view representation, where features and reference frame are modeled as a collection of distinct views, or by a viewpoint invariant representation, where features and reference frame are modeled independently of viewpoint.  compare multi-view and viewpoint invariant representations trained and tested on the same data, where the viewpoint invariant approach results in fer false positive detections and higher average precision.  present a new, iterative learning algorithm to determine an optimal viewpoint invariant reference frame from training images in a data-driven manner. The learned optimal reference frame is centrally located with respect to the 3D object class and to image features in a given view, thereby minimizing reference frame localization error as predicted by theory and maintaining a consistent geometrical interpretation with respect to the underlying object class. Modeling and detection based on the optimal reference frame improves detection performance for both multiview and viewpoint invariant representations. Experimentation is performed on the class of 3D faces, using the public color FERET database for training, the CMU profile database for testing and SIFT image features."}]