シングルスキャン点群データのための半形状幾何学モデル

抄録

The article addresses topics about 3D geometric fitting for multiple objects and describes novel hemi-form geometric models for single-scan point clouds and probabilistic formulation with a combination of Gaussian mixture models and EM algorithm. The hemi-form models assign larger residuals to the observations locating behind a geometric model so as to reflect the fact that those observations in single-scan data are unlikely to belong to that model. Since our models such as hemisphere, hemi-cylinder, and hemi-cone models, allow the range of residuals to be symmetric, we can assume Gaussian distributions as a probabilistic model of the residuals in the strict sense. Calculation of geometric model parameters is implemented by a nonlinear least-squares method and initial value estimation techniques. The proposed framework was applied to both synthetic and real world point clouds involving multiple objects as well as outliers and was found to return acceptable model estimation accuracy results.