Unsupervised Point Cloud Reconstruction via Recurrent Multi-step Moving Strategy
Authors: Liu, Z., Zhang, J., Zhang, M., Ke, R., Yu, C. and Liu, L.
Journal: IEEE Transactions on Multimedia
eISSN: 1941-0077
ISSN: 1520-9210
DOI: 10.1109/TMM.2025.3632687
Abstract:Point cloud reconstruction is an ingredient in geometry modeling, computer graphics, and 3D vision. In this paper, we propose a novel unsupervised learning method called the Recurrent Multi-Step Moving Strategy, which progressively moves query points toward the underlying surface to accurately learn unsigned distance fields (UDFs) for point cloud reconstruction. Specifically, we design a recurrent network for UDF estimation that integrates a multi-step strategy for query movement. This model treats query movement as a trajectory prediction process, establishing dependencies between the current query move decision and the previous path, thus utilizing temporal information to improve UDF estimation accuracy. Further, we design distance and gradient regularization losses to ensure the precision, consistency, and continuity of the estimated UDFs. Extensive evaluations, comparisons, and ablation studies are conducted to show the superiority of our method over the competing approaches in terms of reconstruction accuracy and generality. Our unsupervised reconstruction method outperforms many supervised techniques and demonstrates efficacy across diverse scenarios, including single-object, indoor, and outdoor benchmarks.
Source: Scopus