Optimal Transport Approaches for Cloud Fusion in 3D Reconstruction

Abstract

Cloud fusion plays a crucial role in achieving accurate 3D models. This paper introduces two optimal transport-based approaches, Gaussian Fusion and Cyclical Fusion, to showcase the effectiveness of optimal transport in establishing dense correspondence among incoming 3D point clouds. These approaches encompass several important components. Firstly, the fusion correspondences are derived from the optimal transport problem. The core principle of Gaussian Fusion and Cyclical Fusion involves manipulating points along geodesic curves to effectively leverage significant local geometric information. Secondly, this paper explores the fundamental concepts underlying Gaussian Fusion (displacement interpolation) and Cyclical Fusion (cyclical monotonicity), which greatly enhance the accuracy and completeness of the reconstruction process. And provides evidence for the uniqueness of displacement interpolation as geodesics on L2-Wasserstein space and the rationality of cyclical monotonicity in this context. Finally, the proposed approaches excel in capturing intricate surface details, particularly on small objects, when compared to the original fusion scheme which often introduces severe artifacts.