Out-of-Core Surface Reconstruction via Global TGV Minimization

Nikolai Poliarnyi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 5641-5650

Abstract

We present an out-of-core variational approach for surface reconstruction from a set of aligned depth maps. Input depth maps are supposed to be reconstructed from regular photos or/and can be a representation of terrestrial LIDAR point clouds. Our approach is based on surface reconstruction via total generalized variation minimization (TGV) because of its strong visibility-based noise-filtering properties and GPU-friendliness. Our main contribution is an out-of-core OpenCL-accelerated adaptation of this numerical algorithm which can handle arbitrarily large real-world scenes with scale diversity.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Poliarnyi_2021_ICCV, author = {Poliarnyi, Nikolai}, title = {Out-of-Core Surface Reconstruction via Global TGV Minimization}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {5641-5650} }