Point Cloud Bayesian Reconstruction Based on Particle Filtering

To solve the problem of feature distortion induced by excessive smoothing or sharpening during the reconstruction of 3D point cloud, a feature-preserving probabilistic reconstruction method is proposed. First, low-rank estimation of local curvature of point cloud is realized by space-based principal component analysis in order to better locate sharp features, and the normal field of point cloud is adaptively optimized based on curvature which guarantees accurate normal constraints during sharp feature recovery. Then, the probability reconstruction model of point cloud is established according to Bayesian statistic inference. And the posterior probability distribution of each point position is simulated by sequential Monte Carlo method within particle filtering framework. In order to improve the convergence, the resampling range is adjusted according to the local noise. Finally, the Bayesian reconstruction of point cloud is achieved using resampling with normal and spherical constraints for different regions which takes the advantage of multiple hypothesis testing, and the problems of feature degradation and irregular edges are solved. Compared with several state-of-the-art methods, the experimental results show that the proposed algorithm has improved subjective visual effects, reconstruction accuracy, and efficiency, and the reconstruction error is reduced by 46.84% while the running time is decreased by 33.34% on average. The proposed method can effectively restore original features while suppressing noise, which improve the quality of point cloud.