An Improved Discriminative Optimization for 3D Rigid Point Cloud Registration
This is an incremental improvement for 3D point cloud registration, potentially benefiting computer vision and robotics applications.
The paper tackled 3D rigid point cloud registration by extending the Discriminative Optimization algorithm's histogram from front-back to include up-down and clockwise-anticlockwise sides and reweighting it based on model points' distribution, achieving comparable performance in accuracy and root-mean-square error on the Stanford Bunny and Oxford SensatUrban datasets.
The Discriminative Optimization (DO) algorithm has been proved much successful in 3D point cloud registration. In the original DO, the feature (descriptor) of two point cloud was defined as a histogram, and the element of histogram indicates the weights of scene points in "front" or "back" side of a model point. In this paper, we extended the histogram which indicate the sides from "front-back" to "front-back", "up-down", and "clockwise-anticlockwise". In addition, we reweighted the extended histogram according to the model points' distribution. We evaluated the proposed Improved DO on the Stanford Bunny and Oxford SensatUrban dataset, and compared it with six classical State-Of-The-Art point cloud registration algorithms. The experimental result demonstrates our algorithm achieves comparable performance in point registration accuracy and root-mean-sqart-error.