Jean‐Emmanuel Deschaud

2.0k total citations · 1 hit paper
31 papers, 843 citations indexed

About

Jean‐Emmanuel Deschaud is a scholar working on Computer Vision and Pattern Recognition, Environmental Engineering and Geology. According to data from OpenAlex, Jean‐Emmanuel Deschaud has authored 31 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 14 papers in Environmental Engineering and 13 papers in Geology. Recurrent topics in Jean‐Emmanuel Deschaud's work include Remote Sensing and LiDAR Applications (14 papers), 3D Surveying and Cultural Heritage (13 papers) and Robotics and Sensor-Based Localization (11 papers). Jean‐Emmanuel Deschaud is often cited by papers focused on Remote Sensing and LiDAR Applications (14 papers), 3D Surveying and Cultural Heritage (13 papers) and Robotics and Sensor-Based Localization (11 papers). Jean‐Emmanuel Deschaud collaborates with scholars based in France, United States and Spain. Jean‐Emmanuel Deschaud's co-authors include François Goulette, Xavier Roynard, Beatriz Marcotegui, Hugues Thomas, Andrés Serna, Santiago Velasco-Forero, David Prasser, Brett Browning, Peter Rander and Silvère Bonnabel and has published in prestigious journals such as SHILAP Revista de lepidopterología, The International Journal of Robotics Research and Remote Sensing.

In The Last Decade

Jean‐Emmanuel Deschaud

31 papers receiving 810 citations

Hit Papers

CT-ICP: Real-time Elastic... 2022 2026 2023 2024 2022 50 100 150

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jean‐Emmanuel Deschaud France 13 479 430 307 259 240 31 843
François Goulette France 15 486 1.0× 461 1.1× 332 1.1× 265 1.0× 261 1.1× 36 898
Mihai Dolha Germany 4 309 0.6× 336 0.8× 287 0.9× 130 0.5× 365 1.5× 5 849
Ignacio Vizzo Germany 12 575 1.2× 457 1.1× 775 2.5× 325 1.3× 722 3.0× 18 1.4k
Louis Wiesmann Germany 14 241 0.5× 225 0.5× 459 1.5× 94 0.4× 357 1.5× 27 715
Daniel F. Huber United States 11 362 0.8× 348 0.8× 604 2.0× 98 0.4× 590 2.5× 14 1.1k
Timo Häckel Germany 11 483 1.0× 475 1.1× 128 0.4× 203 0.8× 164 0.7× 21 762
Yuchun Huang China 14 356 0.7× 424 1.0× 102 0.3× 387 1.5× 289 1.2× 47 1.0k
Huan Luo China 22 516 1.1× 364 0.8× 171 0.6× 105 0.4× 439 1.8× 56 1.3k
Jan Elseberg Germany 14 446 0.9× 540 1.3× 590 1.9× 110 0.4× 494 2.1× 24 1.1k
Zhihua Wang United Kingdom 6 803 1.7× 802 1.9× 272 0.9× 797 3.1× 546 2.3× 9 1.5k

Countries citing papers authored by Jean‐Emmanuel Deschaud

Since Specialization
Citations

This map shows the geographic impact of Jean‐Emmanuel Deschaud's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jean‐Emmanuel Deschaud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean‐Emmanuel Deschaud more than expected).

Fields of papers citing papers by Jean‐Emmanuel Deschaud

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean‐Emmanuel Deschaud. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jean‐Emmanuel Deschaud. The network helps show where Jean‐Emmanuel Deschaud may publish in the future.

Co-authorship network of co-authors of Jean‐Emmanuel Deschaud

This figure shows the co-authorship network connecting the top 25 collaborators of Jean‐Emmanuel Deschaud. A scholar is included among the top collaborators of Jean‐Emmanuel Deschaud based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jean‐Emmanuel Deschaud. Jean‐Emmanuel Deschaud is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Deschaud, Jean‐Emmanuel, et al.. (2025). COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving. IEEE Transactions on Robotics. 41. 1742–1754. 1 indexed citations
2.
Deschaud, Jean‐Emmanuel, et al.. (2025). RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis. arXiv (Cornell University). 1808–1817. 2 indexed citations
3.
Deschaud, Jean‐Emmanuel, et al.. (2024). ParisLuco3D: A High-Quality Target Dataset for Domain Generalization of LiDAR Perception. IEEE Robotics and Automation Letters. 9(6). 5496–5503. 1 indexed citations
4.
Deschaud, Jean‐Emmanuel, et al.. (2023). Multi-IMU Proprioceptive State Estimator for Humanoid Robots. 10880–10887. 5 indexed citations
5.
Deschaud, Jean‐Emmanuel, et al.. (2023). MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization. 5765–5772. 5 indexed citations
6.
Nasreddine, Jad, et al.. (2023). 5GMED Seamless Connectivity for Digital Trains. 1–6. 2 indexed citations
7.
Deschaud, Jean‐Emmanuel, et al.. (2022). CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure. 2022 International Conference on Robotics and Automation (ICRA). 5580–5586. 170 indexed citations breakdown →
8.
Deschaud, Jean‐Emmanuel, et al.. (2021). 3D Point Cloud Registration with Multi-Scale Architecture and\n Unsupervised Transfer Learning. arXiv (Cornell University). 20 indexed citations
9.
Deschaud, Jean‐Emmanuel, et al.. (2021). Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping. Remote Sensing. 13(22). 4713–4713. 42 indexed citations
10.
Veltkamp, Remco C., Bas Boom, Santiago Velasco-Forero, et al.. (2020). SHREC 2020: 3D point cloud semantic segmentation for street scenes. Computers & Graphics. 93. 13–24. 17 indexed citations
11.
Roynard, Xavier, Jean‐Emmanuel Deschaud, & François Goulette. (2018). Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification. 2108–21083. 31 indexed citations
12.
Roynard, Xavier, Jean‐Emmanuel Deschaud, & François Goulette. (2017). Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification. arXiv (Cornell University). 219 indexed citations
13.
Deschaud, Jean‐Emmanuel, et al.. (2017). Automatic Ground Surface Reconstruction from mobile laser systems for driving simulation engines. SIMULATION. 93(3). 201–211. 5 indexed citations
14.
Roynard, Xavier, et al.. (2016). FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS. SHILAP Revista de lepidopterología. XLI-B3. 693–699. 12 indexed citations
15.
Deschaud, Jean‐Emmanuel, et al.. (2016). POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM. SHILAP Revista de lepidopterología. XLI-B3. 359–366. 3 indexed citations
16.
Deschaud, Jean‐Emmanuel, et al.. (2016). POINT CLOUD REFINEMENT WITH A TARGET-FREE INTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM. SPIRE - Sciences Po Institutional REpository. 3 indexed citations
17.
Roynard, Xavier, et al.. (2016). FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS. ˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences. XLI-B3. 693–699. 8 indexed citations
18.
Deschaud, Jean‐Emmanuel, et al.. (2015). TARGET-FREE EXTRINSIC CALIBRATION OF A MOBILE MULTI-BEAM LIDAR SYSTEM. SHILAP Revista de lepidopterología. II-3/W5. 97–104. 5 indexed citations
19.
Bonnabel, Silvère, et al.. (2014). Experimental implementation of an Invariant Extended Kalman Filter-based scan matching SLAM. 4121–4126. 8 indexed citations
20.
Browning, Brett, Jean‐Emmanuel Deschaud, David Prasser, & Peter Rander. (2012). 3D Mapping for high-fidelity unmanned ground vehicle lidar simulation. The International Journal of Robotics Research. 31(12). 1349–1376. 22 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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