ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 151-157, 2015
https://doi.org/10.5194/isprsannals-II-3-W5-151-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
19 Aug 2015
3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA
S. Hosseinyalamdary and A. Yilmaz Photogrammetric Computer Vision (PCV) Lab 2070 Neil Avenue, Columbus, OH 43212, USA
Keywords: 3D Super-resolution, Geometric Surface Reconstruction, Diffusion Equations, isotropic and anisotropic Abstract. Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.
Conference paper (PDF, 14020 KB)


Citation: Hosseinyalamdary, S. and Yilmaz, A.: 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 151-157, https://doi.org/10.5194/isprsannals-II-3-W5-151-2015, 2015.

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