Automatic Occlusion Removal from Facades for 3D Urban Reconstruction

Engels, Chris; Tingdahl, David; Vercruysse, Mathias; Tuytelaars, Tinne; Sahli, Hichem; Gool, Luc Van

doi:10.1007/978-3-642-23687-7_61

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…The detailed results, including the overall accuracy, ACC, and MCC greater than 85% and +0. 6, respectively, clearly demonstrate the efficacy of the proposed method. Figure 7 shows the progressive reduction of the size of the similarity map in subsequent passages.…”

Section: Change Detection and Reset Functionsupporting

confidence: 52%

“…Wang et al [5] extended this approach to also infer depth from stereo pairs. Wang et al [6] used a similar method for 3D data, but occlusions were found automatically using object-specific detectors, making it more suitable for larger data sets. Several works have suggested that manual workload can be greatly reduced by using interactive methods that allow a user to quickly mark foreground and background areas, while exact segmentations are determined using graph cuts.…”

Section: Related Workmentioning

confidence: 99%

“…In these measures, we use Tversky's model to compare the two sets of attributes describing the individuals; the function, f , of this model is the Minkowski's formula, as rewritten in Equation (6). The parameter, p, of this formula equals one, since in Tversky's model, the function, f , corresponds to a linear combination of the features.…”

Section: Similarity Map Constructionmentioning

confidence: 99%

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Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating

2013

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In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified into three main object classes: permanently static, temporarily static and mobile, using a new point matching technique. The temporarily static and mobile objects are then removed from the 3D point clouds, leaving behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from successive passages (in the same place) on different days and at different times are then matched together to complete the 3D urban landscape. The changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity, and the resulting 3D cartography is progressively modified accordingly. The specialized functions introduced help to remove the different imperfections, due to occlusions, misclassifications and different changes occurring in the environment over time, thus ncreasing the robustness of the method. The results, evaluated on real data, demonstrate that not only is the resulting 3D cartography accurate, containing only the exact permanent features free from imperfections, but the method is also suitable for handling large urban scenes.Remote Sens. 2013, 5 3702

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Section: Change Detection and Reset Functionsupporting

confidence: 52%

Section: Related Workmentioning

confidence: 99%

Section: Similarity Map Constructionmentioning

confidence: 99%

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Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating

2013

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“…This approach was extended by (Wang et al, 2008) to also infer depth from stereo pairs. A similar method was used by (Engels et al, 2011) for 3D data, but occlusions were found automatically using object-specific detectors making it more suitable for larger data sets. In the context of building facades and urban reconstruction, increased contextual knowledge is available by assuming structure's planarity and repetition of features such as floors, windows, etc.…”

Section: Introductionmentioning

confidence: 99%

Automatic Update and Completion of Occluded Regions for Accurate 3D Urban Cartography by combining Multiple Views and Multiple Passages

Aijazi

Checchin

Trassoudaine

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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Handling occlusions is one of the more difficult challenges faced today in urban landscape analysis and cartography. In this paper, we successfully address this problem by using a new method in which multiple views and multiple sessions or passages are used to complete occluded regions in a 3D cartographic map. Two 3D point clouds, from different viewing angles, obtained in each passage are first classified into two main object classes: Permanent and Temporary (which contains both Temporarily static and Mobile objects) using inference based on basic reasoning. All these Temporary objects, considered as occluding objects, are removed from the scene leaving behind two perforated 3D point clouds of the cartography. These two perforated point clouds from the same passage are then combined together to fill in some of the holes and form a unified perforated 3D point cloud of the cartography. This unified perforated 3D point cloud is then updated by similar subsequent perforated point clouds, obtained on different days and hours of the day, filling in the remaining holes and completing the missing features/regions of the urban cartography. This automatic method ensures that the resulting 3D point cloud of the cartography is most accurate containing only the exact and actual permanent features/regions. Special update and reset functions are added to increase the robustness of the method. The method is evaluated on a standard data set to demonstrate its efficacy and prowess.

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Handling Occlusions for Accurate 3D Urban Cartography: A New Approach Based on Characterization and Multiple Passages

Aijazi

Checchin

Trassoudaine

2012

2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization &Amp; Transmission

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Automatic Occlusion Removal from Facades for 3D Urban Reconstruction

Cited by 3 publications

References 16 publications

Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating

Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating

Automatic Update and Completion of Occluded Regions for Accurate 3D Urban Cartography by combining Multiple Views and Multiple Passages

Handling Occlusions for Accurate 3D Urban Cartography: A New Approach Based on Characterization and Multiple Passages

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