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Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 63 - 75 Titre : Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis Type de document : Article/Communication Auteurs : Max Mehltretter, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 63 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] corrélation épipolaire dense [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] courbe épipolaire [Termes descripteurs IGN] disparité [Termes descripteurs IGN] effet de profondeur cinétique [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] modèle d'incertitude [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] voxel Résumé : (auteur) Motivated by the need to identify erroneous disparity estimates, various methods for the estimation of aleatoric uncertainty in the context of dense stereo matching have been presented in recent years. Especially, the introduction of deep learning based methods and the accompanying significant improvement in accuracy have greatly increased the popularity of this field. Despite this remarkable development, most of these methods rely on features learned from disparity maps only, neglecting the corresponding 3-dimensional cost volumes. However, conventional hand-crafted methods have already demonstrated that the additional information contained in such cost volumes are beneficial for the task of uncertainty estimation. In this paper, we combine the advantages of deep learning and cost volume based features and present a new Convolutional Neural Network (CNN) architecture to directly learn features for the task of aleatoric uncertainty estimation from volumetric 3D data. Furthermore, we discuss and apply three different uncertainty models to train our CNN without the need to provide ground truth for uncertainty. In an extensive evaluation on three datasets using three common dense stereo matching methods, we investigate the effects of these uncertainty models and demonstrate the generality and state-of-the-art accuracy of the proposed method. Numéro de notice : A2021-012 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.003 date de publication en ligne : 18/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96415 [article]

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Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 201 - 215 Titre : Context pyramidal network for stereo matching regularized by disparity gradients Type de document : Article/Communication Auteurs : Junhua Kang, Auteur ; Lin Chen, Auteur ; Fei Deng, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 201 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] appariement de formes [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] gradient [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] vision stéréoscopique Résumé : (Auteur) Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas. To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas. We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods. Numéro de notice : A2019-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.012 date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93729 [article]

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Modelling of buildings from aerial LiDAR point clouds using TINs and label maps / Minglei Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 127 - 138 Titre : Modelling of buildings from aerial LiDAR point clouds using TINs and label maps Type de document : Article/Communication Auteurs : Minglei Li, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 127 - 138 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] modèle numérique du bâti [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] toit [Termes descripteurs IGN] Triangulated Irregular Network Résumé : (Auteur) This paper presents a new framework for automatically creating compact building models from aerial LiDAR point clouds, where each point is known to belong to the class building. The approach addresses the issues of non-uniform point density and outlier detection to extract and refine semantic roof structures by a sequence of operations on a label map. We first partition the points into some coarse regions based on a region growing method over the Triangulated Irregular Network (TIN) model. The region label IDs are then projected to a 2D grid map, which is used to refine the roof regions and their boundaries. We design an energy optimization approach on the label map to optimize the region labels. In order to regularize the contours of roof regions extracted from the label map, we propose a new method for refining contour segment vertices, which iteratively filters the normals of contour segments and uses them to guide the update of contour vertices. The effectiveness of this method is evaluated on LiDAR point clouds from different scenes, and its performance is validated by extensive comparisons to state-of-the-art techniques. Numéro de notice : A2019-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.003 date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93082 [article]

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Robust structure from motion based on relative rotations and tie points / Xin Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 5 (May 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 5 (May 2019) . - pp 347 - 359 Titre : Robust structure from motion based on relative rotations and tie points Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 347 - 359 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] compensation locale par faisceaux [Termes descripteurs IGN] équation linéaire [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] point de liaison (imagerie) [Termes descripteurs IGN] rotation [Termes descripteurs IGN] structure-from-motion Résumé : (Auteur) In this article, we present two new approaches for image orientation with a focus on robustness, starting with relative orientations of available image pairs, an incremental and a global one, and compare their performance. For the incremental approach, we first choose a suitable initial image pair, and we then iteratively extend the image cluster by adding new images. The rotations of these newly added images are estimated from relative rotations by single rotation averaging. In the next step, a linear equation system is set up for each new image to solve the translation parameters with triangulated tie points that can be viewed in that new image, followed by a resection for refinement. Finally, we refine the orientation parameters of the images by a local bundle adjustment. We also present a global method that consists of two parts: global rotation averaging, followed by setting up a large linear equation system to solve for all image translation parameters simultaneously; a final bundle adjustment is carried out to refine the results. We compare these two methods by analyzing results on different benchmark sets, including ordered and unordered image data sets from the Internet and two other challenging data sets to demonstrate the performance of our two approaches. We conclude that while the incremental method typically yields results of higher accuracy and performs better on the challenging data sets, our global method runs significantly faster. Numéro de notice : A2019-438 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.5.347 date de publication en ligne : 01/05/2019 En ligne : https://doi.org/10.14358/PERS.85.5.347 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92769 [article]

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Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation / Xin Wang in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 19 - 41 Titre : Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation Type de document : Article/Communication Auteurs : Xin Wang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] matrice de rotation [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] recouvrement d'images [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters. We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness. Numéro de notice : A2019-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.009 date de publication en ligne : 15/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91970 [article]

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A higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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IV-1/W1 - ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1 [30/05/2017]) / Christian Heipke  

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Information from imagery: ISPRS scientific vision and research agenda / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 115 (May 2016)  

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vol 115 - May 2016 - State-of-the-art in photogrammetry, remote sensing and spatial information science (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Christian Heipke  

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II-3 W4 - March 2015 - [actes] PIA15+HRIGI15 Joint ISPRS conference, 25 - 27 March 2015; Munich, Germany (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Uwe Stilla  

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vol 100 - February 2015 - High-resolution Earth imaging for geospatial information (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Christian Heipke  

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An overview of close-range photogrammetry in France / Raphaële Heno in Revue Française de Photogrammétrie et de Télédétection, n° 200 (Novembre 2012)  

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Matching of straight line segments from aerial stereo images of urban areas / A. Ok in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)

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Road network extraction in suburban areas / A. Grote in Photogrammetric record, vol 27 n° 137 (March - May 2012)  

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Relevance assessment of full-waveform lidar data for urban area classification / Clément Mallet in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

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