Abstract:
This paper presents a novel unified one-stage unsupervised learning framework forpoint cloud cleaning of noisy partial data from underwater side-scan sonars. By combining a swath-based point cloud tensor representation, an adaptive multi-scale feature encoder, and a generative Bayesian framework, the proposed method provides robust sonarpoint cloud denoising, completion, and outlier removal simultaneously. The condensed swath-based tensor representation preserves point cloud of underlying three-dimensionalgeometry of point cloud by reconstructing spatial and temporal correlation of sonar data.The adaptive multi-scale feature encoder distinguishes noisy partial tensor data without handcrafted feature labeling by utilizing CANDECOMP/PARAFAC tensor factorization. Each local embedded outlier feature under various scales is aggregated into aglobal context by a generative Bayesian framework. The model is automatically inferredby a variational Bayesian, without parameter tuning and model pre-training. Extensive experiments on large scale synthetic and real data demonstrates the robustness against environmental perturbation. The proposed algorithm compares favourably with existing methods.
