Out-of-Core Surface Reconstruction via Global $TGV$ Minimization
This addresses the challenge of processing large-scale 3D data for applications like photogrammetry or LIDAR scanning, though it is incremental as it adapts an existing method for scalability.
The paper tackles the problem of surface reconstruction from large-scale depth maps by developing an out-of-core variational approach based on total generalized variation minimization, enabling handling of arbitrarily large real-world scenes with GPU acceleration.
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.