Point Cloud and 3D Modeling (NPM3D)



We created NPM3D Benchmark Suite on 3D Point Clouds:

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Research Team NPM3D


Research areas of the NMP3D team are around 3D data as “point cloud”:

 – Mobile LiDAR systems
 – Point cloud rendering (screen-space rendering, splatting)
 – LiDAR SLAM, Localization on Map
 – HD Maps for autonomous vehicles
 – Point Cloud processings from archeological sites / objects
 – Object recognition / scene segmentation and classification in LiDAR data
 – Deep Learning for 3D Point Clouds

We have multiple 3D sensors (Kinect 2, scanner FARO Focus X130, Velodyne VLP16, HDL32) and two mobile mapping platforms:
 – L3D2: vehicle with GPS/IMU localization system, Velodyne HDL32 and Ladybug5
 – Drone with Velodyne VLP16 and Camera FLIR Blackfly




 • Researchers:
       – François Goulette: Full Professor,
       – Jean-Emmanuel Deschaud: Associate Professor,

 • PhD students:
       – Jules Sanchez (2020-): Real-time semantic segmentation of lidar data for the autonomous vehicle
       – Pierre Dellenbach (2020-): Self-supervised Deep SLAM on camera and LiDAR data
       – Jean-Pierre Richa (2019-): Point Cloud rendering through splatting for LiDAR Simulation 
       – Sofiane Horache (2019-): Automatic recognition of patterns on 3D curved surfaces and its application to Celtic art
       – David Duque (2018-): Segmentation and classification of road scenes by analysis of LIDAR data, color and multi-spectral images (in cooperation with CMM lab)

 • Former PhD students:
       – Hugues Thomas (2016-2019): Learning new representations for 3D point cloud semantization (now Post-Doc at University of Toronto)
       – Xavier Roynard (2015-2019): Semantization of 3D Points Clouds acquired by Embedded System (working now at SAFRAN as R&D engineer)
       – Hassan Bouchiba (2014-2018): Contributions in point-based processing for rendering and simulation in fluid mechanics (working now at Terra3D)
       – Houssem Nouira (2013-2016): Point cloud refinement with self-calibration of a mobile multi-beam lidar (working now at MENSI/TRIMBLE)


Selected Papers




Automatic clustering of celtic coins based on 3D Point Cloud Pattern Analysis, ISPRS Congress, 2020, pdf, video




SHREC 2020 Track: 3D Point Cloud Semantic Segmentation for Street Scenes, Computer & Graphics, 2020, pdf





Computational Fluid Dynamics on 3D Point Set Surfaces, Journal of Computational Physics, 2020, pdf






KPConv: Flexible and Deformable Convolution for Point Clouds, ICCV, 2019, pdf, code






A Graph-based Color Lines model for image analysis, ICIAP, 2019, pdf








Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification, IJRR, 2018, pdf








Classification of Point Cloud for Road Scene Understanding with Multiscale Voxel Deep Network, Workshop IROS, 2018, pdf, code







Semantic Classification of 3D Point Clouds with Multiscale Spherical Neighborhoods, 3DV, 2018, pdf







Raw point cloud deferred shading through screen space pyramidal operators, Short Paper EUROGRAPHICS, 2018, video, pdf 








IMLS-SLAM: scan-to-model matching based on 3D data, ICRA, 2018, pdf









Point Cloud Refinement with Self-calibration of a mobile multi-beam Lidar Sensor, The Photogrammetric Record, 2017, pdf






Current research projects





REPLICA : Massive computing platform for autonomous vehicle with realistic sensors simulation







ARCHEO 3D : Automatic recognition of shapes and more particularly carved patterns repeated by stamping on different celtic scanned object








 • Course on 3D Point Clouds in the Master 2 MVA at ENS Paris-Saclay and Master 2 IASD at PSL University: course webpage here



If you do not know “3D Point Cloud data”





Point Cloud of MINES ParisTech (FARO Focus X130)







Point Cloud of MINES ParisTech (FARO Focus X130)







 Point Cloud of the city of Lille (our plateform L3D2 with Velodyne HDL32)