Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-resolution Model for Multi-class Volumetric Labeling
Abstract
We propose an adaptive multi-resolution formulation of semantic 3D reconstruction. Given a set of images of a scene, semantic 3D reconstruction aims to densely reconstruct both the 3D shape of the scene and a segmentation into semantic object classes. Jointly reasoning about shape and class allows one to take into account class-specific shape priors (e.g., building walls should be smooth and vertical, and vice versa smooth, vertical surfaces are likely to be building walls), leading to improved reconstruction results. So far, semantic 3D reconstruction methods have been limited to small scenes and low resolution, because of their large memory footprint and computational cost. To scale them up to large scenes, we propose a hierarchical scheme which refines the reconstruction only in regions that are likely to contain a surface, exploiting the fact that both high spatial resolution and high numerical precision are only required in those regions. Our scheme amounts to solving a sequence of convex optimizations while progressively removing constraints, in such a way that the energy, in each iteration, is the tightest possible approximation of the underlying energy at full resolution. In our experiments the method saves up to 98% memory and 95% computation time, without any loss of accuracy.
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Remote sensing is key to inferring infrastructure data, as satellite data are globally available and largely consistent.
“Remote sensing is key to inferring infrastructure data [79,224,339,496,535,868] as satellite data 19 present a source of information that is globally available and largely consistent worldwide.”
From Tackling climate change with machine learning (2023) · cites this paper as [79]
Cites
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/37401.37422 · Cited by 10,143 · see citing works
Bradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994 · see citing works
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Chambolle, A., & Pock, T. (2011) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-010-0251-1 · Cited by 4,336 · see citing works
Self-citationStereo Processing by Semiglobal Matching and Mutual Information
Hirschmüller, H. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,199 · see citing works
A volumetric method for building complex models from range images
Curless, B., & Levoy, M. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237269 · Cited by 3,124 · see citing works
Accurate, Dense, and Robust Multiview Stereopsis
Furukawa, Y., & Ponce, J. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.161 · Cited by 3,031 · see citing works
Poisson Surface Reconstruction
Kazhdan, M., Bolitho, M., & Hoppe, H. (2006) · doi:10.2312/sgp/sgp06/061-070 · Cited by 2,186 · see citing works
A Multigrid Tutorial, Second Edition
Briggs, W. L., Henson, V. E., & McCormick, S. F. (2000) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898719505 · Cited by 1,516 · see citing works
VisualSFM:A visual structure from motion system,
Wu, C. (2011) · Medical Entomology and Zoology · Cited by 673 · see citing works
A Method for Finding Projections onto the Intersection of Convex Sets in Hilbert Spaces
Boyle, J. P., & Dykstra, R. L. (1986) · Lecture notes in statistics · doi:10.1007/978-1-4613-9940-7_3 · Cited by 423 · see citing works
Diagonal preconditioning for first order primal-dual algorithms in convex optimization
Pock, T., & Chambolle, A. (2011) · International Conference on Computer Vision · doi:10.1109/iccv.2011.6126441 · Cited by 402 · see citing works
Self-citationMulti-view reconstruction preserving weakly-supported surfaces
Jančošek, M., & Pajdla, T. (2011) · doi:10.1109/cvpr.2011.5995693 · Cited by 374 · see citing works
A multigrid tutorial (2nd ed.)
Briggs, W. L., Henson, V. E., & McCormick, S. F. (2000) · Society for Industrial and Applied Mathematics eBooks · Cited by 359 · see citing works
Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation
Lafarge, F., & Mallet, C. (2012) · International Journal of Computer Vision · doi:10.1007/s11263-012-0517-8 · Cited by 325 · see citing works
Grinspun, E., Krysl, P., & Schröder, P. (2002) · ACM SIGGRAPH Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) · doi:10.1145/566570.566578 · Cited by 260 · see citing works
A convex relaxation approach for computing minimal partitions
Pock, T., Chambolle, A., Cremers, D., & Bischof, H. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206604 · Cited by 252 · see citing works
Self-citationPiecewise planar and non-planar stereo for urban scene reconstruction
Gallup, D., Frahm, J., & Pollefeys, M. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539804 · Cited by 242 · see citing works
SSD: Smooth Signed Distance Surface Reconstruction
Calakli, F., & Taubin, G. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.02058.x · Cited by 229 · see citing works
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video
Kundu, A., Li, Y., Dellaert, F., Li, F., & Rehg, J. M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_45 · Cited by 220 · see citing works
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration
Zach, C., Pock, T., & Bischof, H. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408983 · Cited by 220 · see citing works
Self-citationJoint 3D Scene Reconstruction and Class Segmentation
Häne, C., Zach, C., Cohen, A., Angst, R., & Pollefeys, M. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.20 · Cited by 213 · see citing works
Labatut, P., Pons, J.-P., & Keriven, R. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408892 · Cited by 209 · see citing works
Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
Vineet, V., Miksik, O., Lidegaard, M., Niebner, M., Golodetz, S., Prisacariu, V. A., Kahler, O., Murray, D. W., Izadi, S., Peerez, P., et al. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7138983 · Cited by 200 · see citing works
Efficient gradient-domain compositing using quadtrees
Agarwala, A. (2007) · ACM SIGGRAPH Conference Papers (SIGGRAPH) · doi:10.1145/1275808.1276495 · Cited by 126 · see citing works
Multiview Stereo and Silhouette Consistency via Convex Functionals over Convex Domains
Cremers, D., & Kolev, K. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.174 · Cited by 112 · see citing works
Fast and High Quality Fusion of Depth Maps
Zach, C. (2008) · Cited by 104 · see citing works
Hornung, A., & Kobbelt, L. P. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.135 · Cited by 97 · see citing works
Multilevel streaming for out-of-core surface reconstruction
Bolitho, M., Kazhdan, M. M., Burns, R. C., & Hoppe, H. (2007) · doi:10.5555/1281991.1282001 · Cited by 89 · see citing works
Unconstrained isosurface extraction on arbitrary octrees
Kazhdan, M. M., Klein, A. W., Dalal, K., & Hoppe, H. (2007) · doi:10.5555/1281991.1282009 · Cited by 85 · see citing works
MULTIBOOST: a multi-purpose boosting package
Benbouzid, D., Busa‐Fekete, R., Casagrande, N., Collin, F.-D., & Kégl, B. (2012) · Cited by 83 · see citing works
Fast Joint Estimation of Silhouettes and Dense 3D Geometry from Multiple Images
Kolev, K., Brox, T., & Cremers, D. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.150 · Cited by 77 · see citing works
A Hybrid Multiview Stereo Algorithm for Modeling Urban Scenes
Lafarge, F., Keriven, R., Brédif, M., & Vu, H. H. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.84 · Cited by 62 · see citing works
Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction
Ladický, Ľ., Sturgess, P., Russell, C., Sengupta, S., Baştanlar, Y., Clocksin, W. F., & Torr, P. H. S. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.104 · Cited by 62 · see citing works
Discrete optimization of ray potentials for semantic 3D reconstruction
Savinov, N., Ladický, Ľ., Häne, C., & Pollefeys, M. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299190 · Cited by 48 · see citing works
Fast and Accurate Large-Scale Stereo Reconstruction Using Variational Methods
Kuschk, G., & Cremers, D. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.96 · Cited by 28 · see citing works
Fast Generation of Pointerless Octree Duals
Lewiner, T., Mello, V. M., Peixoto, A., Pesco, S., & Lopes, H. (2010) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2010.01775.x · Cited by 28 · see citing works
Probabilistic Labeling Cost for High-Accuracy Multi-view Reconstruction
Kostrikov, I., Horbert, E., & Leibe, B. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.199 · Cited by 27 · see citing works
An Efficient Algorithm to Calculate the Minkowski Sum of Convex 3D Polyhedra
Bekker, H., & Roerdink, J. B. T. M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45545-0_71 · Cited by 26 · see citing works
Semantically-Aware Aerial Reconstruction from Multi-modal Data
Cabezas, R., Straub, J., & Fisher, J. S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.249 · Cited by 25 · see citing works
Ray Markov Random Fields for image-based 3D modeling: Model and efficient inference
Liu, S., & Cooper, D. B. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539790 · Cited by 23 · see citing works
Zach, C., Häne, C., & Pollefeys, M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.105 · Cited by 22 · see citing works
Tight convex relaxations for vector-valued labeling problems
Strekalovskiy, E., Goldlüecke, B., & Cremers, D. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126514 · Cited by 21 · see citing works
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ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368 · see citing works
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400 · see citing works
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Yin, Z., & Shi, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00212 · Cited by 1,280 · see citing works
Semantic Scene Completion from a Single Depth Image
Song, S., Yu, F., Zeng, A., Chang, A. X., Savva, M., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.28 · Cited by 1,244 · see citing works
Computer Vision for Autonomous Vehicles
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000079 · Cited by 664 · see citing works
ScanNet: Richly-annotated 3D Reconstructions of Indoor ScenesPreprint
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T. A., & Nießner, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.04405 · Cited by 537 · see citing works
LiDAR Point Clouds to 3-D Urban Models$:$ A Review
Wang, R., Peethambaran, J., & Chen, D. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2781132 · Cited by 320 · see citing works
3D change detection – Approaches and applications
Qin, R., Tian, J., & Reinartz, P. H. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.09.013 · Cited by 292 · see citing works
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · doi:10.1561/9781680836899 · Cited by 243 · see citing works
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Kundu, A., Genova, K., Yin, X., Fathi, A., Pantofaru, C., Guibas, L. J., Tagliasacchi, A., Dellaert, F., & Funkhouser, T. A. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01253 · Cited by 223 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
OctNetFusion: Learning Depth Fusion from Data
Riegler, G., Ulusoy, A. O., Bischof, H., & Geiger, A. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00017 · Cited by 184 · see citing works
Machine learning for geographically differentiated climate change mitigation in urban areas
Milojevic-Dupont, N., & Creutzig, F. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102526 · Cited by 160 · see citing works
Automated digital modeling of existing buildings: A review of visual object recognition methods
Czerniawski, T., & Leite, F. L. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103131 · Cited by 117 · see citing works
Two Stream 3D Semantic Scene Completion
Garbade, M., Chen, Y.-T., Sawatzky, J., & Gall, J. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00055 · Cited by 99 · see citing works
TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo
Romanoni, A., & Matteucci, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01051 · Cited by 97 · see citing works
Robust point cloud classification based on multi-level semantic relationships for urban scenes
Zhu, Q., Li, Y., Hu, H., & Wu, B. (2017) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2017.04.022 · Cited by 95 · see citing works
Large-Scale Structure from Motion with Semantic Constraints of Aerial Images
Chen, Y., Wang, Y., Lu, P., Chen, Y., & Wang, G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03398-9_30 · Cited by 96 · see citing works
Dense Semantic 3D Reconstruction
Häne, C., Zach, C., Cohen, A., & Pollefeys, M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2613051 · Cited by 84 · see citing works
See and Think: Disentangling Semantic Scene Completion
Liu, S., Hu, Y. H., Zeng, Y., Tang, Q., Jin, B., Han, Y., & Li, X. (2018) · neural information processing systems · Cited by 84 · see citing works
RoutedFusion: Learning Real-Time Depth Map Fusion
Weder, S., Schönberger, J. L., Pollefeys, M., & Oswald, M. R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00494 · Cited by 85 · see citing works
A deep learning approach to DTM extraction from imagery using rule-based training labels
Gevaert, C. M., Persello, C., Nex, F., & George Vosselman, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.06.001 · Cited by 79 · see citing works
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A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
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Multi-Label Semantic 3D Reconstruction Using Voxel Blocks
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Shamshiri, R., Motagh, M., Nahavandchi, H., Haghighi, M. H., & Hoseini, M. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111608 · Cited by 54 · see citing works
Visual-Inertial-Semantic Scene Representation for 3D Object Detection
Dong, J., Fei, X., & Soatto, S. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.380 · Cited by 47 · see citing works
Learning Priors for Semantic 3D Reconstruction
Cherabier, I., Schönberger, J. L., Oswald, M. R., Pollefeys, M., & Geiger, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_20 · Cited by 46 · see citing works
Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method
Widyaningrum, E., Bai, Q., Fajari, M. K., & Lindenbergh, R. (2021) · Remote Sensing · doi:10.3390/rs13050859 · Cited by 41 · see citing works
Semantically Derived Geometric Constraints for MVS Reconstruction of Textureless Areas
Stathopoulou, E. K., Battisti, R., Cernea, D. C., Fabio Remondino, A., & Georgopoulos, A. (2021) · Remote Sensing · doi:10.3390/rs13061053 · Cited by 33 · see citing works
Semantic scene completion with dense CRF from a single depth image
Zhang, L., Wang, L., Zhang, X., Shen, P., Bennamoun, M., Zhu, G., Shah, S. A. A., & Song, J. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.052 · Cited by 31 · see citing works
Lotte, R. G., Norbert Haala, A., Karpina, M., Aragão, L. E. O. C., & Shimabukuro, Y. E. (2018) · Remote Sensing · doi:10.3390/rs10091435 · Cited by 30 · see citing works
Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization
Holzmann, T., Maurer, M., Fraundorfer, F., & Bischof, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_29 · Cited by 28 · see citing works
Detail Preserved Surface Reconstruction from Point Cloud
Zhou, Y., Shen, S., & Hu, Z. (2019) · Sensors · doi:10.3390/s19061278 · Cited by 28 · see citing works
Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization
Armagan, A., Hirzer, M., Roth, P. M., & Lepetit, V. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.488 · Cited by 27 · see citing works
3-D Instance Segmentation of MVS Buildings
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Bulatov, D., Burkard, E., Ilehag, R., Kottler, B., & Helmholz, P. (2020) · Infrared Physics & Technology · doi:10.1016/j.infrared.2020.103233 · Cited by 25 · see citing works
Multi-view Stereo with Single-View Semantic Mesh Refinement
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DSEM-NeRF: Multimodal feature fusion and global–local attention for enhanced 3D scene reconstruction
Liu, D., Wang, Z., & Chen, P. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102752 · Cited by 24 · see citing works
The role of machine intelligence in photogrammetric 3D modeling – an overview and perspectives
Qin, R., & Gruen, A. (2020) · International Journal of Digital Earth · doi:10.1080/17538947.2020.1805037 · Cited by 24 · see citing works
Li, J., Huang, X., Feng, Y., Ji, Z., Zhang, S., & Wen, D. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3234694 · Cited by 23 · see citing works
S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point CloudsPreprint
Cheng, R., Agia, C., Ren, Y., Li, X., & Bingbing, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09242 · Cited by 26 · see citing works
Use of active learning for earthquake damage mapping from UAV photogrammetric point clouds
Xu, Z., Wu, L. X., & Zhang, Z. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1466083 · Cited by 22 · see citing works
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Chen, Y.-T., Garbade, M., & Gall, J. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803174 · Cited by 18 · see citing works
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Stucker, C., Richard, A., Wegner, J. D., & Schindler, K. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-263-2018 · Cited by 17 · see citing works
Self-citationSelf-Supervised Learning for Stereo Reconstruction on Aerial Images
Knöbelreiter, P., Vogel, C., & Pock, T. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518316 · Cited by 17 · see citing works
Self-citationHuang, X., Cao, R., & Cao, Y. (2018) · Journal of the Indian Society of Remote Sensing · doi:10.1007/s12524-018-0911-y · Cited by 17 · see citing works
Semi-supervised Semantic Mapping Through Label Propagation with Semantic Texture Meshes
Rosu, R. A., Quenzel, J., & Behnke, S. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01187-z · Cited by 17 · see citing works
OctNetFusion: Learning Depth Fusion from DataPreprint
Riegler, G., Ulusoy, A. O., Bischof, H., & Geiger, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01047 · Cited by 15 · see citing works
GEOMETRIC PRIMITIVE EXTRACTION FROM SEMANTICALLY ENRICHED POINT CLOUDS
Kyriakaki-Grammatikaki, S., Stathopoulou, E. K., Grilli, E., Fabio Remondino, A., & Georgopoulos, A. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlvi-2-w1-2022-291-2022 · Cited by 16 · see citing works
MF3D: Model-free 3D semantic scene parsing
Tung, F., & Little, J. J. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989534 · Cited by 15 · see citing works
TOWARDS MESH-BASED DEEP LEARNING FOR SEMANTIC SEGMENTATION IN PHOTOGRAMMETRY
Knott, M., & Groenendijk, R. W. (2021) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2021-59-2021 · Cited by 13 · see citing works
Gao, X., Cui, H., Zhu, L., Shi, T., & Shen, S. (2019) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2019.08.003 · Cited by 13 · see citing works
Semantically Informed Multiview Surface Refinement
Blaha, M., Rothermel, M., Oswald, M. R., Sattler, T., Richard, A., Wegner, J. D., Pollefeys, M., & Schindler, K. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.412 · Cited by 11 · see citing works
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Wang, S., Liu, X., Zhang, Y., Li, J., Zou, S., Wu, J., Tao, C., Liu, Q., & Cai, G. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103324 · Cited by 11 · see citing works
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Zhou, Y., Shen, S., & Hu, Z. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00066 · Cited by 10 · see citing works
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Lv, M., Tu, D., Tang, X., Liu, Y., & Shen, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561077 · Cited by 9 · see citing works
Wang, S., Guo, J., Zhang, Y., & Wu, Y. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.01.022 · Cited by 9 · see citing works
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Wahbeh, W., Ammann, M., Nebiker, S., van Eggermond, M. A. B., & Erath, A. (2021) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-4-2021-225-2021 · Cited by 8 · see citing works
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Wei, R., Pei, H., Wu, D., Zeng, C., Ai, X., & Duan, H. (2024) · Applied Sciences · doi:10.3390/app14052218 · Cited by 7 · see citing works
Learned Semantic Multi-Sensor Depth Map Fusion
Rozumnyi, D., Cherabier, I., Pollefeys, M., & Oswald, M. R. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00264 · Cited by 7 · see citing works
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery
Zhang, Y., Chen, G., Chen, J., & Cui, S. R. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01993 · Cited by 6 · see citing works
Ruf, B., Erdnüß, B., & Martin Weinmann, A. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w6-325-2017 · Cited by 6 · see citing works
Li, S., Liu, Z., Shen, Z., & Cheng, K.-T. T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20026 · Cited by 6 · see citing works
Omrany, H., Ghaffarianhoseini, A., GhaffarianHoseini, A., Dhawan, K., Almhafdy, A., & Oteng, D. (2024) · Urban sustainability · doi:10.1007/978-981-99-9014-6_11 · Cited by 5 · see citing works
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Romanoni, A., & Matteucci, M. (2018) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2018.00081 · Cited by 5 · see citing works
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Zhao, J., Xue, B., Vennell, R., & Zhang, M. (2023) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz61134.2023.10343634 · Cited by 4 · see citing works
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Häne, C., & Pollefeys, M. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900143 · Cited by 4 · see citing works
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Wegen, O., Döllner, J., & Richter, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10536-4_39 · Cited by 4 · see citing works
Efficient Surface-Aware Semi-Global Matching with Multi-View Plane-Sweep Sampling
Ruf, B., Pollok, T., & Martin Weinmann, A. (2019) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000104539 · Cited by 4 · see citing works
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Richard, A., Vogel, C., Blaha, M., Pock, T., & Schindler, K. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.98 · Cited by 3 · see citing works
Self-citationA survey of recent 3D scene analysis and processing methods
Yuan, J., Abdul-Rashid, H., & Li, B. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10615-7 · Cited by 3 · see citing works
Unmanned aerial vehicle mapping for settlement upgrading
Gevaert, C. M. (2018) · doi:10.3990/1.9789036546355 · Cited by 3 · see citing works
On the Importance of 3D Surface Information for Remote Sensing Classification Tasks
Petrich, J., Sander, R., Bradley, E. S., Dawood, A., & Hough, S. (2021) · Data Science Journal · doi:10.5334/dsj-2021-020 · Cited by 2 · see citing works
Active Semantic Labeling of Street View Point Clouds
Zhou, Y., Shen, S., & Hu, Z. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00274 · Cited by 2 · see citing works
van de Wouw, D. W. J. M., ter Haar, F. B., Dubbelman, G., & de With, P. H. N. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.4.043009 · Cited by 2 · see citing works
EFFICIENT SURFACE-AWARE SEMI-GLOBAL MATCHING WITH MULTI-VIEW PLANE-SWEEP SAMPLING
Ruf, B., Pollok, T., & Martin Weinmann, A. (2019) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-w7-137-2019 · Cited by 2 · see citing works
AN EFFICIENT REPRESENTATION OF 3D BUILDINGS: APPLICATION TO THE EVALUATION OF CITY MODELS
Ennafii, O., Bris, A. L., Lafarge, F., & Mallet, C. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2021-329-2021 · Cited by 2 · see citing works
Fusion SLAM technology based on 3D pixel projection and optical flow enhancement mechanism
Qin, Y., Zhou, W., Liu, J., & Chen, L. (2025) · International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) · doi:10.1109/aiahpc66801.2025.11290062 · Cited by 1 · see citing works
On the Role of Representations for Reasoning in Large-Scale Urban Scenes
Cabezas, R., Blaha, M., Zheng, S., Rosman, G., Schindler, K., & Fisher, J. S. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.195 · Cited by 1 · see citing works
Self-citationA REGION-BASED MACHINE LEARNING APPROACH FOR SELF-DIAGNOSIS OF A 4D DIGITAL THERMAL TWIN
Strauß, E., & Bulatov, D. (2022) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-4-w2-2022-265-2022 · Cited by 1 · see citing works
A Data-driven Prior on Facet Orientation for Semantic Mesh LabelingPreprint
Romanoni, A., & Matteucci, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09999
SAM-MSF:A multi-scale indoor point cloud segmentation method based on deep learning
Zhang, Y., & Wang, W. (2025) · International Symposium on Computer Applications and Information Systems (ISCAIS) · doi:10.1109/iscait64916.2025.11010263
Semantically-Enhanced RGB-D 3D Reconstruction for Pavement Crack Digital Archiving
Liang, 赵. Z., Yutong, J., Liu, S., Dengfeng, C., & Zhen, Z. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11560505
- Authors
- Blaha, M., Vogel, C., Richard, A., Wegner, J. D., Pock, T., & Schindler, K.
- Affiliations
- ETH Zurich (CH)
- AIT Austrian Institute of Technology GmbH (AT)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3176-3184
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2460551350
- Cited by
- 88
- References
- 43
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 8 · 2021: 8 · 2020: 9 · 2019: 13 · 2018: 18 · 2017: 16 · 2016: 5
- FWCI
- 31.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- semantic 3D reconstruction, multi-resolution reconstruction, multi-class segmentation, hierarchical refinement, convex optimization, energy minimization, memory efficiency, large-scale scenes, shape priors
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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