In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular Bees Algorithm. Tested on three sets of clean and differently blurred point cloud models, the Bees Algorithm obtained performances comparable to those obtained using the state-of-the-art RANSAC method, and superior to those obtained by an evolutionary algorithm. Shape fitting times were compatible with real-time application. The main advantage of the Bees Algorithm over standard methods is that it doesn't rely on ad hoc assumptions about the nature of the point cloud model like RANSAC approximation tolerance.
|Journal||Applied Sciences (Switzerland)|
|Early online date||29 Nov 2019|
|Publication status||E-pub ahead of print - 29 Nov 2019|
- machine vision
- primitive fitting
- bees algorithm