Robust Unsupervised Cleaning of Underwater Bathymetric Point Cloud Data

A. Lynn Abbott

Abstract

This paper presents a novel unified one-stage unsupervised learning framework for point cloud cleaning of noisy partial data from underwater side-scan sonars. By com- bining a swath-based point cloud tensor representation, an adaptive multi-scale feature encoder, and a generative Bayesian framework, the proposed method provides robust sonar point cloud denoising, completion, and outlier removal simultaneously. The con- densed swath-based tensor representation preserves the point cloud associated with the underlying three-dimensional geometry by utilizing spatial and temporal correlation of sonar data. The adaptive multi-scale feature encoder identifies noisy partial tensor data without handcrafted feature labeling by utilizing CANDECOMP/PARAFAC tensor fac- torization. Each local embedded outlier feature under various scales is aggregated into a global context by a generative Bayesian framework. The model is automatically inferred by a variational Bayesian, without parameter tuning and model pre-training. Extensive experiments on large scale synthetic and real data demonstrate robustness against environmental perturbation. The proposed algorithm compares favourably with existing methods.

Cong Chen, Abel Gawel, Stephen Krauss, Yuliang Zou, A. Lynn Abbott, Daniel J. Stilwell: Robust Unsupervised Cleaning of Underwater Bathymetric Point Cloud Data. BMVC 2020