High-Performance Multi-View Reconstruction

Zach, Christopher; Sormann, Mario; Karner, Konrad

doi:10.1109/3dpvt.2006.74

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Therefore, considering a series of 2D images acquired from different observation points, by the use of SfM approach, it is possible to obtain the reconstruction of three-dimensional models and the production of sparse point cloud [ 28 ]. After this step, it is possible to run a dense reconstruction phase using Multi-View Stereo (MVS) [ 29 ]; this latter step allows obtaining a dense point cloud. In general, the several processing steps that lead to the construction of the model and implemented in many commercial software are: (i) alignment of the images; (ii) building a dense point cloud (PC) and (iii) building mesh [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry

2020

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The aim of this study is to evaluate the degradation of the accuracy and quality of the images in relation to the TIFF format and the different compression level of the JPEG format compared to the raw images acquired by UAV platform. Experiments were carried out using DJI Mavic 2 Pro and Hasselblad L1D-20c camera on three test sites. Post-processing of images was performed using software based on structure from motion and multi-view stereo approaches. The results show a slight influence of image format and compression levels in flat or slightly flat surfaces; in the case of a complex 3D model, instead, the choice of a format became important. Across all tests, processing times were found to also play a key role, especially in point cloud generation. The qualitative and quantitative analysis, carried out on the different orthophotos, allowed to highlight a modest impact in the use of the TIFF format and a strong influence as the JPEG compression level increases.

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Section: Methodsmentioning

confidence: 99%

Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry

2020

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“…As soon as the foreground object is extracted an optional GPU-based dense matching [19] is initiated. Moreover, after volumetric range image integration we obtain a complete 3D model of the extracted foreground object.…”

Section: Methodsmentioning

confidence: 99%

Graph Cut Based Multiple View Segmentation for 3D Reconstruction

Sormann

Zach

Karner

2006

Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)

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In this paper we propose a novel framework for efficiently extracting foreground objects in so called shortbaseline image sequences. We apply the obtained segmentation to improve subsequent 3D reconstruction results. Essentially, our framework combines a graph cut based optimization algorithm with an intuitive user interface. At first a meanshift segmentation algorithm partitions each image of the sequence into a certain number of regions. Additionally we provide an intelligent graphical user interface for easy specification of foreground as well as background regions across all images of the sequence. Within the graph cut optimization algorithm we define new energy terms to increase the robustness and to keep the segmentation of the foreground object coherent across all images of the sequence. Finally, a refined graph cut segmentation and several adjustment operations allow an accurate and effective foreground extraction. The obtained results are demonstrated on several real world data sets.

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“…On the other hand, for a long time, the estimation of surface radiance/reflectance was secondary and was mainly of use to set up the shape reconstruction task (Faugeras and Keriven 1998;Zickler 2006;Zickler et al 2002). Even some very recent works (Pons et al 2005Goesele et al 2006;Zach et al 2006;Tran and Davis 2006;Kolev et al 2007bKolev et al , 2007a compute the 3D shape without considering radiance estimation. However, radiance/reflectance estimation has become a matter of concern in multiview reconstruction scenarios in the last decade.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions

2009

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We propose a generative model based method for recovering both the shape and the reflectance of the surface(s) of a scene from multiple images, assuming that illumination conditions and cameras calibration are known in advance. Based on a variational framework and via gradient descents, the algorithm minimizes simultaneously and consistently a global cost functional with respect to both shape and reflectance. The motivations for our approach are threefold.(1) Contrary to previous works which mainly consider specific individual scenarios, our method applies indiscriminately to a number of classical scenarios; in particular it works for classical stereovision, multiview photometric stereo and multiview shape from shading. It works with changing as well as static illumination. (2) Our approach naturally combines stereo, silhouette and shading cues in a single framework. (3) Moreover, unlike most previous methods dealing with only Lambertian surfaces, the proposed method considers general dichromatic surfaces. We verify the method using various synthetic and real data sets.

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High-Performance Multi-View Reconstruction

Cited by 9 publications

References 15 publications

Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry

Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry

Graph Cut Based Multiple View Segmentation for 3D Reconstruction

Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions

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