Haala , Rothermel 119 Image-based 3 D Data Capture in Urban Scenarios

Presuming that airborne imagery is available at a sufficient overlap, state-of-the-art multi-stereo matching can generate DSM raster representations at an accuracy and resolution which corresponds to the ground sampling distance (GSD) of the original images. For such secenarios recent matching software exploits the resulting redundancy and derives surface representations at a remarkable accuracy and reliability. Typically, DSM rasters are generated as a standard result at a grid size corresponding to the average pixel footprint by a rather simple fusion of the 3D point clouds from multi-view matching. While such 2.5D models are suitable for a number of applications, high resolution data capture in complex urban environments requires the reconstruction and representation of 3D representations. This is especially true while aiming at the geometric reconstruction of objects with distinct structure like urban furniture or building façades. After a brief introduction in the state-of-the-art on high density image matching for DSM computation, this generation of filtered point clouds and 3D meshes within our multi-view reconstruction pipeline is discussed for both imagery aerial cameras and camera based mobile mapping systems. The results are especially beneficial while aiming at high quality visualisations and geometric data capture in urban scenarios.

[1]  Renato Pajarola Large scale terrain visualization using the restricted quadtree triangulation , 1998 .

[2]  Michael Goesele,et al.  Let There Be Color! Large-Scale Texturing of 3D Reconstructions , 2014, ECCV.

[3]  M. Rothermel,et al.  Generating Oriented Pointsets From Redundant Depth Maps Using Restricted Quadtrees , 2014 .

[4]  Norbert Haala,et al.  An update on automatic 3D building reconstruction , 2010 .

[5]  Norbert Haala,et al.  Dense Multi-Stereo Matching for High Quality Digital Elevation Models , 2012 .

[6]  H. Hirschmüller Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Stereo Processing by Semi-global Matching and Mutual Information , 2022 .

[7]  Mathias Rothermel,et al.  Fast and Robust Generation of Semantic Urban Terrain Models from UAV Video Streams , 2014, 2014 22nd International Conference on Pattern Recognition.

[8]  Michael M. Kazhdan,et al.  Screened poisson surface reconstruction , 2013, TOGS.

[9]  Dieter Fritsch,et al.  Stereo Model Selection and Point Cloud Filtering using an Out-of-Core Octree , 2014 .

[10]  M. Rothermel,et al.  Benchmarking High Density Image Matching for Oblique Airborne Imagery , 2014 .

[11]  William Ribarsky,et al.  Real-time, continuous level of detail rendering of height fields , 1996, SIGGRAPH.

[12]  Norbert Haala,et al.  FAÇADE RECONSTRUCTION USING GEOMETRIC AND RADIOMETRIC POINT CLOUD INFORMATION , 2015 .

[13]  M. Rothermel,et al.  SURE : PHOTOGRAMMETRIC SURFACE RECONSTRUCTION FROM IMAGER Y , 2013 .

[14]  Florent Lafarge,et al.  Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation , 2012, International Journal of Computer Vision.

[15]  Mathias Rothermel,et al.  EVALUATION OF MATCHING STRATEGIES FOR IMAGE-BASED MOBILE MAPPING , 2015 .