Surface reconstruction from point clouds using a novel variational model


Multi-view reconstruction has been an active research topic in the computer vision community for decades. However, state of the art 3D reconstruction systems have lacked the speed, accuracy, and ease to use properties required by the industry. The work described in this paper is part of the effort to produce a multi-view reconstruction system for a UK company. A novel variational level set method is developed for reconstructing an accurate implicit surface for a set of unorganised points (point cloud). The variational model consists of three energy terms to ensure accurate and smooth surface reconstruction whilst preserving the fine details of the point cloud and increasing speed. The model also completely eliminated the need for reinitialisation associated with the level set method. Implementation details of the variational model using gradient descent optimisation are given, and the roles of its three energy terms are illustrated through numerical experiments. Experiments show that the proposed method outperformed the state of the art surface reconstruction approaches.

International Conference on Innovative Techniques and Applications of Artificial Intelligence