Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Registration of partial scan data sets is still a challenge for today's CAD systems and CAD system users. Many of the known methods rely on user interaction or feature recognition. For non-regular users this is too time consuming and error prone. The paper describes a method to register partial scan data by fitting a large fat tetrahedron (LFT) in the target point cloud. The method is computational intensive and in its CPU implementation not fit for interactive use. The independency of the points in the data-sets makes massive parallel computing applicable. The paper describes the implementation of the method on a GTX260 GPU using the CUDA programming environment. A performance gain of 10 times compared to a conventional CPU implementation was achieved, which can be further improved by implementing a pre selection method of the result, bringing interactive use in range after further optimization.
['Adrie Kooijman', 'Joris S. M. Vergeest']
I am writing a research paper on a idea GPU Implementation of the LFT Shape Matching Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea GPU Implementation of the LFT Shape Matching Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Registration of partial scan data sets is still a challenge for today's CAD systems and CAD system users. Many of the known methods rely on user interaction or feature recognition. For non-regular users this is too time consuming and error prone. The paper describes a method to register partial scan data by fitting a large fat tetrahedron (LFT) in the target point cloud. The method is computational intensive and in its CPU implementation not fit for interactive use. The independency of the points in the data-sets makes massive parallel computing applicable. The paper describes the implementation of the method on a GTX260 GPU using the CUDA programming environment. A performance gain of 10 times compared to a conventional CPU implementation was achieved, which can be further improved by implementing a pre selection method of the result, bringing interactive use in range after further optimization."}]