J. Morel, A. Bac, and T. Kanai: Digital terrain model from UAV photogrammetric data. STAG, Smart tools and application in graphics, 2020 (pdf)
This paper presents a method designed to finely approximate ground surfaces from UAV photogrammetric point clouds by
relying on statistical filters to separate vegetation from potential ground points, dividing the whole plot in similar complexity sub-plots through an optimized tilling, and filling holes by blending multiple local approximations through the partition of unity principle. Experiments on very different terrain topology show that our approach leads to significant improvement over the state-of-the-art method.
J. Morel, A. Bac, and T. Kanai: High accuracy terrain reconstruction from point clouds using implicit deformable model. Meshfree, Lecture Notes in Computer Sciences, 2020 (pdf).
This paper presents a method designed to finely approximate ground surfaces by relying on deep learning to separate vegetation from potential ground points, filling holes by blending multiple local approximations through the partition of unity principle, then improving the accuracy of the reconstructed surfaces by pushing the surface towards the data points through an iterative convection model.
J. Morel, A. Bac, and T. Kanai: Segmentation of unbalanced and nn-homogeneous point clouds and its application to 3D scanned trees, The Visual Computer, 2020 (pdf).
Segmentation of 3D point clouds is still an open issue in the case of unbalanced and in-homogeneous data-sets. In the application context of the modeling of botanical trees, a fundamental challenge consists in separating the leaves from the wood. Based on deep learning and a class decision process, we propose an innovative method designed to separate leaf points from wood points in terrestrial LiDAR point clouds of trees. Although simple, our approach learns trees characteristic point patterns efficiently and robustly. To train our 3D deep learning model, we constructed a 3D labeled point cloud data-set of different tree species. Experiments show that our 3D deep representation together with our geometric approach lead to significant improvement over the state-of-the-art methods in segmentation task.
J. Morel, A. Bac, C. Véga: Surface reconstruction of incomplete datasets: a novel Poisson surface approach based on CSRBF, Computer & Graphics, 2018 (pdf).
This paper introduces a novel surface reconstruction method based on unorganized point clouds, which focuses on offering complete and closed mesh models of partially sampled object surfaces. To accomplish this task, our approach builds upon a known a priori model that coarsely describes the scanned object to guide the modeling of the shape. In the region of space visible to the scanner, we retrieve the surface by following the resolution of a Poisson problem. In the occluded region of space, we consider the a priori model as a sufficiently accurate descriptor of the shape. Both models are then blended to obtain a closed model.
J. Morel, A. Bac, C. Véga: Digital terrain model reconstruction from terrestrial LiDAR data using compactly supported radial basis, IEEE Computer Graphics and applications, 2017 (pdf).
This paper introduces a surface approximation algorithm dedicated to extracting digital terrain models from terrestrial laser scanning data acquired in forest areas. The method combines simultaneously terrain model reconstruction and hole filling. It is based on the combination of a quadtree subdivision of space guided by the local density and distribution of data together with a modeling of terrain model via radial basis functions used as partitions of unity for merging local quadratic approximations.
J. Morel: An Android Application to visualize point clouds and meshes in VR, Proceedings of the 11th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, 2017 (pdf).
This paper presents a review of well-known rendering techniques and their adaptation to the features of OpenGL ES 2.0 to develop an Android application dedicated to the visualization of surface meshes and point clouds in virtual reality. Using the headtracking sensors of the smartphone, a generic Bluetooth controller and a virtual reality headset, this application has proven to be a powerful tool to investigate and explore 3D point clouds and meshed surfaces as a VR environment.
J. Morel, A. Bac, C. Véga: Computation of tree volume from terrestrial LiDAR data. Proceedings of the 9th Symposium on Mobile Mapping Technology, MMT 2015, UNSW, Sydney, Australia, 2015.
This paper introduces an original methodological framework to compute an implicit surface of tree woody structure. Relying on the robustness of quantitative structure models to describe a rough tree structure, we replace the cylinders of those models by quadratic local approximations further merged by partition of unity. In doing so, we refine the tree shape reconstruction where data samples are available and preserve the supporting geometrical shape in the occlusions.
Surface Reconstruction Based on Forest Terrestrial LiDAR Data, February 2017 (pdf).
French Institute of Pondicherry, UMIFRE 21 CNRS-MAE, Pondicherry, India.
Laboratoire des Sciences de l’Information et des Systèmes, UMR 7296, Aix Marseille, France.
Xinlian Liang, Juha Hyyppä, Jules Morel, …, Yunsheng Wang: International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of photogrammetry and remote sensing, 2018.
J.Morel, A. Bac, C. Véga: Computation of tree volume from TLS data. Proceedings of Silvilaser, Geospatial Week, La Grande Motte, France, 2015
J. Morel, A. Bac, C. Véga: Forest carbon assessment from LiDAR 3D point cloud analysis. Regional Forum on Climate Change, 2015,AIT, Bangkok, Thailand, 2015