Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces a 3-d representation of vehicles as a space of scale and orientation transformations that define the shape of individual vehicle instances. This shape space forms a group, where the similarity of different vehicle observations can be evaluated using a distance measure defined by Lie group theory. A generic class of vehicles (e.g. SUV) is represented by a set of curves on the Lie group manifold, called geodesics. The classification of any given vehicle instance is achieved by finding the class with the smallest Lie distance beten the geodesics and the vehicle shape. Vehicle recognition is carried out on 3-d LIDAR point clouds. The performance of the Lie classifier is evaluated against two other approaches and found to provide superior recognition performance, particularly with respect to the ability to generalize from a small number of labeled prototypes.
['Pradeep Yarlagadda', 'Özge Can Özcanli', 'Joseph L. Mundy']
I am writing a research paper on a idea Lie group distance based generic 3-d vehicle classification
[{"from": "human", "value": "I am writing a research paper on a idea Lie group distance based generic 3-d vehicle classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces a 3-d representation of vehicles as a space of scale and orientation transformations that define the shape of individual vehicle instances. This shape space forms a group, where the similarity of different vehicle observations can be evaluated using a distance measure defined by Lie group theory. A generic class of vehicles (e.g. SUV) is represented by a set of curves on the Lie group manifold, called geodesics. The classification of any given vehicle instance is achieved by finding the class with the smallest Lie distance beten the geodesics and the vehicle shape. Vehicle recognition is carried out on 3-d LIDAR point clouds. The performance of the Lie classifier is evaluated against two other approaches and found to provide superior recognition performance, particularly with respect to the ability to generalize from a small number of labeled prototypes."}]