# Out-of-Core Surface Reconstruction via Global TGV Minimization

arXiv: https://arxiv.org/abs/2107.14790 · HTML: https://mlsanity.com/paper/2107.14790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 37 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity](https://mlsanity.com/paper/1705.00949/) (2017)

## Built on by (most cited first, 1 of 1)

- [POCO: Point Convolution for Surface Reconstruction](https://mlsanity.com/paper/2201.01831/) (2022)

## Abstract (arXiv)

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.