, 2.3 Tradeoff between region and semantic segmentation, p.110

, Proposed region segmentation method

. .. , Proposed semantic segmentation method

.. .. Experiments,

.. .. Conclusion,

.. .. Problem,

. .. ,

, Range-image disocclusion technique

. .. Results-&-analysis,

.. .. Conclusion,

. .. , 2 2D detection architecture for 3D detection, p.136

. .. Methodology, 3.3 Projection and fusion of the predictions, p.141

. .. Results,

.. .. Conclusion,

[. Bibliography and . Abayowa, Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models, ISPRS Journal of Photogrammetry and Remote Sensing, vol.106, issue.1, pp.68-81, 2015.

. Achanta, SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2008.

D. Aiger, N. J. Mitra, and D. Cohen-or, 4-points congruent sets for robust pairwise surface registration, ACM Transactions on Graphics, vol.27, pp.85-91, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00622443

S. Allaire, J. J. Kim, S. L. Breen, D. A. Jaffray, and V. Pekar, Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis, IEEE Conf. on Computer Vision and Pattern Recognition, pp.1-8, 2006.

G. Aubert, P. Kornprobst, M. Auclair-fortier, D. Ziou, V. Badrinarayanan et al., Mathematical problems in image processing: partial differential equations and the calculus of variations, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.15, issue.12, pp.2481-2495, 2006.

[. Barber, PatchMatch: A randomized correspondence algorithm for structural image editing, Proc. of ECCV, vol.22, pp.404-417, 1996.

J. Becker, C. Stewart, R. J. Radke, R. Benenson, M. Omran et al., Ten years of pedestrian detection, what have we learned?, ECCV European Conference on Computer Vision, vol.1, pp.301-329, 2009.

M. Bertalmio, G. Sapiro, V. Caselles, and C. Ballester, Image inpainting, ACM Comp. graphics and interactive techniques 1, pp.417-424, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00522652

P. J. Besl and N. D. Mckay, Method for registration of 3D shapes, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.1611, pp.586-607, 1992.

[. Bevilacqua, Visibility Estimation and Joint Inpainting of Lidar Depth Maps. In : IEEE Int. Conf. on Image Processing, pp.1-5, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01316719

[. Bevilacqua, Joint inpainting of depth and reflectance with visibility estimation, Remote Sensing and Spatial Information Sciences, vol.125, pp.16-32, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01348304

[. Biasutti, Diffusion anisotrope et inpainting d'orthophotographies LiDAR mobile, RFIA Congrés national sur la Reconnaissance des Formes et l'Intelligence Artificielle, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01316487

[. Biasutti, Disocclusion of 3D LiDAR point clouds using range images, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.4, pp.75-82, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01522366

[. Biasutti, Désoccultation de nuage de points LiDAR en topologie capteur, GRETSI Groupement de Recherche en Traitement du Signal et de l'Image, 2017.

[. Biasutti, Détection et localisation d'objets 3D par apprentissage profond en topologie capteur, Photogrammetric Engineering & Remote Sensing, vol.84, pp.31-40, 2018.

[. Biasutti, Fast image and LiDAR alignment based on 3D rendering in sensor topology, VISAPP International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp.27-35, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02100715

M. Brédif, RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud, 2019.

[. Bichen, Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud, 2018.

[. Bitenc, Evaluation of a LiDAR land-based mobile mapping system for monitoring sandy coasts, Remote Sensing, vol.3, 2011.

A. Bletterer, H. Bouchiba, R. Groscot, J. Deschaud, F. Goulette et al., Une approche basée graphes pour la modélisation et le traitement de nuages de points massifs issus d'acquisitions de LiDARs terrestres, Eurographics Annual Conference of the European Association for Computer Graphics, vol.3, pp.492-526, 2010.

M. Brédif, Image-Based Rendering of LOD1 3D City Models for trafficaugmented Immersive Street-view Navigation, Remote Sensing and Spatial Information Sciences, vol.1, issue.3, pp.7-11, 2013.

[. Brédif, Distributed Dimensionality-Based Rendering of LiDAR Point Clouds, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.3, pp.559-564, 2015.

J. E. Bresenham, A. Briot, P. Viswanath, and S. Yogamani, Analysis of efficient CNN design techniques for semantic segmentation, IEEE Conf. on Computer Vision and Pattern Recognition, vol.4, pp.663-672, 1965.

. Buyssens, Depthaware patch-based image disocclusion for virtual view synthesis, SIGGRAPH International Conference on Computer Graphics and Interactive Techniques, vol.34, pp.2-6, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01206537

, Exemplar-based inpainting: Technical review and new heuristics for better geometric reconstructions, vol.24, pp.1809-1824, 2015.

[. Castorena, Autocalibration of LiDAR and optical cameras via edge alignment, pp.2862-2866, 2011.

A. Chambolle, T. Pock, and . Chan, A first-order primal-dual algorithm for convex problems with applications to imaging, Proc. of ECCV, vol.40, pp.1-12, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00490826

T. F. Chan and S. Esedoglu, Aspects of Total Variation Regularized L1 Function Approximation, SIAM Journal on Imaging Sciences, vol.65, issue.5, pp.1817-1837, 2005.

C. , A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration, SIAM Journal on Imaging Sciences, vol.20, issue.6, pp.1964-1977, 1999.

H. Chen, M. Cheng, J. Li, and Y. Liu, An iterative terrain recovery approach to automated DTM generation from airborne LiDAR point clouds, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.39, 2012.

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.40, issue.4, pp.834-848, 2018.

L. Chen, Y. Zhu, G. Papandreou, F. Schroff, A. Hartwig et al., A point cloud filtering approach to generating DTMs for steep mountainous areas and adjacent residential areas, IEEE Conf. on Computer Vision and Pattern Recognition, vol.8, pp.801-808, 2016.

[. Chen, Region filling and object removal by exemplar-based image inpainting, IEEE Trans. on Image Processing, vol.17, issue.1, pp.1200-1212, 2004.

[. Dai, R-FCN: Object detection via region-based fully convolutional networks, Advances in Neural Inf. Proc. Sys., 2016 [Daribo and Pesquet-Popescu, vol.1, pp.167-170, 2010.

. Delon, A nonparametric approach for histogram segmentation, IEEE Trans. on Image Processing, vol.16, issue.1, pp.253-261, 2007.

D. Doria and R. Radke, Filling large holes in LiDAR data by inpainting depth gradients, Int. Conf. on Pattern Recognition 1, pp.65-72, 1999.

A. A. Efros, T. K. Leung, D. Eigen, C. Puhrsch, R. Fergus et al., Depth map prediction from a single image using a multi-scale deep network, IEEE Conf. on Computer Vision and Pattern Recognition, vol.2, pp.2366-2374, 1999.

. Feng, Fast plane extraction in organized point clouds using agglomerative hierarchical clustering, IEEE International Conference on Robotics and Automation (ICRA), pp.6218-6225, 2014.

. Ferstl, Image guided depth upsampling using anisotropic total generalized variation, IEEE Int. Conf. on Computer Vision, vol.1, pp.993-1000, 1981.

M. A. Fischler and R. C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.

J. H. Friedman, J. L. Bentley, and R. A. Finkel, An algorithm for finding best matches in logarithmic expected time, ACM Transactions on Mathematical Software, vol.3, issue.3, pp.209-226, 1977.

[. Fu, Deep ordinal regression network for monocular depth estimation, IEEE Conf. on Computer Vision and Pattern Recognition, pp.2002-2011, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01741163

[. Furukawa, Y. Ponce-;-furukawa, and J. Ponce, Accurate, dense, and robust multiview stereopsis, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.32, issue.8, pp.1362-1376, 2010.

[. Garcia, , 2010.

F. Garcia, B. Mirbach, B. Ottersten, F. Grandidier, and A. Cuesta, Pixel weighted average strategy for depth sensor data fusion, IEEE Int. Conf. on Image Processing, pp.2805-2808, 2010.

J. Gehrung, M. Hebel, M. Arens, and U. Stilla, An approach to extract moving objects from MLS data using a volumetric background representation, ISPRS International Annals of the Photogrammetry, Remote Sens. and Spatial Inf, vol.4, pp.107-114, 2017.

[. Geiger, Vision meets Robotics: The KITTI Dataset, vol.32, pp.1231-1237, 2013.

[. Geiger, , 2012.

A. Geiger, P. Lenz, and R. Urtasun, Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite, IEEE Conf. on Computer Vision and Pattern Recognition, 2012.

R. Girshick, Fast R-CNN, IEEE Conf. on Computer Vision and Pattern Recognition, 2015.

[. González, An automatic procedure for co-registration of terrestrial laser scanners and digital cameras, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, issue.3, pp.308-316, 2009.

[. Guan, DEM generation from LiDAR data in wooded mountain areas by cross-section-plane analysis, International Journal of Remote Sensing, vol.35, 2014.

S. Guinard, B. Vallet, M. Guislain, J. Digne, R. Chaine et al., Sensor-topology based simplicial complex reconstruction from mobile laser scanning, ISPRS International Annals of the Photogrammetry, Remote Sens. and Spatial Inf, vol.2, pp.90-102, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02181493

A. Harrison and P. Newman, Image and Sparse Laser Fusion for Dense Scene Reconstruction, Field and Service Robotics (FRS), pp.219-228, 2010.

A. Hervieu, B. Soheilian, A. Hervieu, and B. Soheilian, Semi-automatic road/pavement modeling using mobile laser scanning, ISPRS International Annals of the Photogrammetry, vol.4, pp.1247-1252, 2013.

[. Hervieu, Road Marking Extraction Using a Model&Data-driven RJ-MCMC, ISPRS International Annals of the Photogrammetry, Remote Sens. and Spatial Inf, vol.2, pp.47-48, 2015.

, LIDAR-based 3D object perception, Proceedings of international workshop on Cognition for Technical Systems, pp.1-7, 2008.

[. Hu, Squeeze-and-excitation networks, IEEE Conf. on Computer Vision and Pattern Recognition, pp.7132-7141, 2018.

[. Hu, Semi-global filtering of airborne LiDAR data for fast extraction of digital terrain models, Remote Sensing, vol.7, 2015.

H. Huang, S. Wu, D. Cohen-or, M. Gong, H. Zhang et al., L1-medial skeleton of point cloud, ACM Transactions on Graphics, pp.65-72, 2013.

J. Huang and C. Menq, Automatic data segmentation for geometric feature extraction from unorganized 3D coordinate points, IEEE Trans. on Robotics and Automation, vol.17, issue.3, pp.268-279, 2001.

[. Huhle, Fusion of range and color images for denoising and resolution enhancement with a non-local filter, Computer Vision and Image Understanding, vol.114, pp.24-36, 2007.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization

J. J. Koenderink, The structure of images, Biological cybernetics, vol.50, pp.363-370, 1984.

[. Kolb, Time-of-Flight Cameras in Computer Graphics, Computer Graphics Forum, vol.29, pp.141-159, 2001.

K. Kraus, N. Pfeifer, and . Ku, Joint 3D proposal generation and object detection from view aggregation, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.34, pp.23-30, 2001.

F. Lafarge and P. Alliez, Surface reconstruction through point set structuring, Computer Graphics Forum, pp.225-234, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00768197

L. Landrieu, M. Boussaha, C. Ledig, L. Theis, F. Huszár et al., Photo-realistic single image super-resolution using a generative adversarial network, IEEE Conf. on Computer Vision and Pattern Recognition, 2017.

. Lepetit, E-PnP: An accurate o(n) solution to the PnP problem, Int. Jour. of Computer Vision, vol.81, issue.2, p.155, 2009.

B. Li, 3D fully convolutional network for vehicle detection in point cloud, IEEE Trans. on Intelligent Robots and Systems, pp.1513-1518, 2017.

[. Li, Globfit: Consistently fitting primitives by discovering global relations, ACM Trans. on Graphics, pp.52-64, 2011.

[. Li, 2D-3D fusion for layer decomposition of urban facades, IEEE Int. Conf. on Computer Vision, pp.882-889, 2011.

M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, Multi-Task Multi-Sensor Fusion for 3D Object Detection, IEEE Conf. on Computer Vision and Pattern Recognition, pp.7345-7353, 2019.

. Lin, Refinenet: Multi-path refinement networks for high-resolution semantic segmentation, IEEE Conf. on Computer Vision and Pattern Recognition, pp.1925-1934, 2017.

. Lin, Feature pyramid networks for object detection, IEEE Conf. on Computer Vision and Pattern Recognition, 2017.

, 2D Image Processing Applied to 3D LiDAR Point Clouds

T. Lin, P. Goyal, R. Girshick, K. He, P. Dollár et al., Microsoft COCO: Common objects in context, IEEE Conf. on Computer Vision and Pattern Recognition, 2014.

. Liu, Parameterization-free projection for geometry reconstruction, Proc. of ECCV, pp.22-28, 2007.

[. Long, Fully convolutional networks for semantic segmentation, IEEE Conf. on Computer Vision and Pattern Recognition, pp.3431-3440, 2015.

[. Lorenzi, Inpainting strategies for reconstruction of missing data in VHR images, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.5, pp.914-918, 2011.

D. G. Lowe, W. Luo, B. Yang, and R. Urtasun, Fast and furious: Real time end-to-end 3D detection, tracking and motion forecasting with a single convolutional net, Distinctive image features from scale-invariant keypoints, vol.60, pp.91-110, 2004.

A. Mastin, J. Kepner, J. Fisher, R. Mehra, P. Tripathi et al., Automatic registration of LIDAR and optical images of urban scenes, CVPR IEEE Conference on Computer Vision and Pattern Recognition, vol.34, pp.219-230, 2009.

[. Meng, Ground filtering algorithms for airborne LiDAR data: A review of critical issues, Remote Sensing, vol.2, 2010.

M. Menze, A. Geiger, K. Mikolajczyk, and C. Schmid, A performance evaluation of local descriptors, IEEE Conf. on Computer Vision and Pattern Recognition, vol.27, pp.1615-1630, 2005.

. Miled, , 2016.

M. Miled, B. Soheilian, E. Habets, B. Vallet, A. Monszpart et al., Hybrid online mobile laser scanner calibration through image alignment by mutual information, ISPRS Arch. of the Photogrammetry, vol.3, p.12, 2015.

J. Morel and G. Yu, ASIFT: A new framework for fully affine invariant image comparison, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.438-469, 2009.

. Moussa, An automatic procedure for combining digital images and laser scanner data, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.39, 2012.

M. Muja, D. G. Lowe, M. Nikolova, C. Paganelli, M. Peroni et al., Stereopolis II: A multi-purpose and multi-sensor 3D mobile mapping system for street visualisation and 3D metrology, IEEE International Conference of Engineering in Medicine and Biology Society, vol.36, pp.69-79, 2004.

J. Papon, A. Abramov, M. Schoeler, and F. Worgotter, Voxel cloud connectivity segmentation-supervoxels for point clouds, IEEE Conf. on Computer Vision and Pattern Recognition, vol.1, pp.2027-2034, 2013.

[. Park, Scale-space and edge detection using anisotropic diffusion, IEEE Int. Conf. on Computer Vision, vol.2, pp.629-639, 1990.

F. Pierre, J. Aujol, A. Bugeau, N. Papadakis, V. Ta et al., Real-time rendering of massive unstructured raw point clouds using screen-space operators, Eurographics International Conference on Virtual Reality, Archaeology and Cultural Heritage, vol.8, pp.105-112, 2011.

S. Pu, M. Rutzinger, G. Vosselman, S. O. Elberink, C. R. Qi et al., Recognizing basic structures from mobile laser scanning data for road inventory studies, IEEE Conf. on Computer Vision and Pattern Recognition, vol.66, pp.28-39, 2011.

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, Pointnet: Deep learning on point sets for 3D classification and segmentation, IEEE Conf. on Computer Vision and Pattern Recognition, pp.652-660, 2017.

[. Qu, Vehicle localization using monocamera and geo-referenced traffic signs, IEEE Intelligent Vehicles Symposium, vol.4, pp.605-610, 2015.

[. Rabbani, Segmentation of point clouds using smoothness constraint, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.36, pp.248-253, 2006.

J. Rabin, G. Peyré, J. Delon, and M. Bernot, Wasserstein barycenter and its application to texture mixing, Scale Space and Variational Methods in Computer Vision, 2011.

. Redmon, You only look once: Unified, real-time object detection, IEEE Conf. on Computer Vision and Pattern Recognition, pp.779-788, 2016.

J. Redmon and A. Farhadi, YOLO9000: Better, Faster, Stronger. In : IEEE Conf. on Computer Vision and Pattern Recognition, 2017.

. [ren, Accurate single stage detector using recurrent rolling convolution, IEEE Conf. on Computer Vision and Pattern Recognition, 2017.

. [ren, Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.39, issue.6, 2015.

Z. Ren, J. Yuan, J. Meng, Z. Zhang, O. Ronneberger et al., U-net: Convolutional networks for biomedical image segmentation, MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention, vol.15, pp.234-241, 2013.

V. Roshni and K. Revathy, Using mutual information and cross correlation as metrics for registration of images, Journal of Theoretical & Applied Information Technology, vol.4, issue.6, 2008.

F. Rottensteiner and C. Briese, A new method for building extraction in urban areas from high-resolution LiDAR data, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.34, pp.295-301, 2002.

. Bibliography-[rubinstein, Improved seam carving for video retargeting, ACM Trans. on Graphics, vol.17, issue.9, pp.1-16, 2008.

E. ;. Rublee, . Rabaud, K. ;. Vincent-;-konolige, and G. Bradski, ORB: An efficient alternative to SIFT or SURF, IEEE Int. Conf. on Computer Vision, pp.2564-2571, 2011.

M. Schmeing and X. Jiang, Depth Image Based Rendering, Pattern Recognition, Machine Intelligence and Biometrics, pp.279-310, 2011.

. Schnabel, Completion and reconstruction with primitive shapes, Computer Graphics Forum, pp.503-512, 2009.

. Schnabel, RANSAC based out-of-core pointcloud shape detection for city-modeling, Proceedings of "Terrestrisches Laserscanning, vol.26, pp.214-226, 2007.

[. Schneider, Semantically Guided Depth Upsampling, German Conference on Pattern Recognition (GCPR), pp.37-48, 2016.

A. Serna and B. Marcotegui, Urban accessibility diagnosis from mobile laser scanning data, Remote Sensing and Spatial Information Sciences, vol.84, pp.23-32, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00963820

A. Serna and B. Marcotegui, Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning, IJPRS International Journal of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.93, pp.243-255, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01010012

J. Serra, Image analysis and mathematical morphology, vol.1, 1982.

S. Shalom, A. Shamir, H. Zhang, and D. Cohen-or, Cone carving for surface reconstruction, ACM Transactions on Graphics, pp.150-160, 2008.

J. Shan and C. K. Toth, Topographic laser ranging and scanning: principles and processing, 2008.

Y. Shao and L. Chen, Automated searching of ground points from airborne LiDAR data using a climbing and sliding method, Photogrammetric Engineering & Remote Sensing, vol.74, 2008.

[. Sharf, Context-based surface completion, ACM Trans. on Graphics, vol.23, issue.3, pp.878-887, 2004.

S. Shi, X. Wang, H. Li, C. Sutour, J. Aujol et al., Denis de : Edge-based multi-modal registration and application for night vision devices, Journal of Mathematical Imaging and Vision, vol.53, issue.2, pp.131-150, 2015.

, 2D Image Processing Applied to 3D LiDAR Point Clouds

A. Tagliasacchi, M. Olson, H. Zhang, G. Hamarneh, and D. Cohen-or, VASE: Volume-Aware Surface Evolution for Surface Reconstruction from Incomplete Point Clouds, Computer Graphics Forum, pp.1563-1571, 2011.

M. Toews, L. Zöllei, W. M. Wells, O. Tournaire, B. Soheilian et al., Towards a subdecimetric georeferencing of groundbased mobile mapping systems in urban areas: Matching ground-based and aerial-based imagery using roadmarks, International Conference on Information Processing in Medical Imaging, vol.36, pp.65-82, 2006.

B. Vallet, Analyse et reconstruction de scènes urbaines, Habilitation à diriger des recherches, 2016.

. Vallet, TerraMobilita/IQmulus urban point cloud analysis benchmark, Computers and Graphics, vol.49, pp.126-133, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01167995

B. Vallet, J. Papelard, and . Velas, Cnn for very fast ground segmentation in velodyne LiDAR data, IEEE International Conference on Autonomous Robot Systems and Competitions, vol.3, pp.137-154, 1997.

R. Wack, A. Wimmer, and . Wang, Digital terrain models from airborne laserscanner data-a grid based approach, ISPRS Arch. of the Photogrammetry, Remote Sens. and Spatial Inf, vol.34, pp.1-8, 2002.

, Weickert 1998] Weickert, J : Anisotropic diffusion in image processing, vol.1, 1998.

[. Wu, Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d LiDAR point cloud, IEEE International Conference on Robotics and Automation (ICRA), pp.1887-1893, 2018.

S. Xiong, J. Zhang, J. Zheng, .. J. Cai, L. Liu et al., Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers, IEEE Conf. on Computer Vision and Pattern Recognition, vol.18, pp.201-205, 2014.

Y. , Fusion of median and bilateral filtering for range image upsampling, IEEE Trans. on Image Processing, vol.22, issue.12, pp.4841-4852, 2013.

M. Johnson, M. N. Do, and . Yuan, Pointseg: Realtime semantic segmentation based on 3d LiDAR point cloud, IEEE Conf. on Computer Vision and Pattern Recognition, pp.5485-5493, 2017.

[. Zach, A globally optimal algorithm for robust TV-L 1 range image integration, ICCV IEEE International Conference on Computer Vision, pp.1-8, 2007.

K. Zak?ek and N. Pfeifer, An improved morphological filter for selecting relief points from a LIDAR point cloud in steep areas with dense vegetation, Technical report: Institute of Anthropological and Spatial Studies, 2006.

J. Zhang and X. Lin, Filtering airborne LiDAR data by embedding smoothness-constrained segmentation in progressive TIN densification, ISPRS Journal of Photogrammetry and Remote Sensing, vol.81, 2012.

Z. Zhang, Microsoft Kinect sensor and its effect, IEEE International Conference on Multimedia and Exposition, vol.19, pp.4-10, 2012.

[. Zhao, Icnet for real-time semantic segmentation on high-resolution images, Proc. of ECCV, pp.405-420, 2018.

Y. Zhou, O. Tuzel, and . Zhu, Segmentation and classification of range image from an intelligent vehicle in urban environment, IEEE Conf. on Computer Vision and Pattern Recognition, vol.1, pp.1457-1462, 2010.

L. Zhuang and J. M. Bioucas-dias, Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.11, 2010.

S. Zinger and L. Do, Free-viewpoint depth image based rendering, Journal of Visual Communication and Image Representation, vol.21, issue.5, pp.533-541, 2010.

. .. , 32 1.4.1 Choice of the approach and requirements

. .. Inpainting-of-occlusions, 35 1.5.1 Occlusion hole detection

. .. Results,

. .. Conclusion, 50 2.1.1 Addressed problem and related works

. .. Model, 53 2.2.1 Visibility-weighted data-fidelity terms

. .. Experimental-results, 57 2.3.1 Quantitative evaluation with a benchmark data set, p.59

.. .. Conclusion, , p.61

, 66 3.4 Visibility estimation dataset for LiDAR point clouds

. .. Experiments-&-results, 70 3.5.1 Evaluation on the Visibility Estimation Dataset

.. .. Conclusion, , p.78

R. .. Experiments,

.. .. Conclusion,

, 2.3 Tradeoff between region and semantic segmentation, p.110

, Proposed region segmentation method

. .. , Proposed semantic segmentation method

.. .. Experiments,

.. .. Conclusion,

.. .. Problem,

. .. ,

, Range-image disocclusion technique

. .. Results-&-analysis,

.. .. Conclusion,

. .. , 2 2D detection architecture for 3D detection, p.136

. .. Methodology, 3.3 Projection and fusion of the predictions, p.141

.. .. Results,