International audienceWe introduce an innovative methodology for reconstructing tree stems from terrestrial LiDAR data. The method uses a multi-scale combination of an original Hough transform and active contours. Tests were conducted on both simulated data and real LiDAR data. Even though no formal evaluation was completed yet, the procedure seems promising

Bac, Alexandra

Fournier, Richard,

Ravaglia, Joris,

Ravaglia, Joris, A

Fournier, Richard, A

HAL AMU

Tree stem reconstruction from terrestrial laser scannerpoint cloud using Hough transform and open activecontoursJoris Ravaglia, Alexandra Bac, Richard FournierTo cite this version:Joris Ravaglia, Alexandra Bac, Richard Fournier. Tree stem reconstruction from terrestriallaser scanner point cloud using Hough transform and open active contours. Silvilaser 2015, Sep2015, La Grande Motte, France. Proceedings of Silvilaser 2015. <hal-01376470>HAL Id: hal-01376470https://hal.archives-ouvertes.fr/hal-01376470Submitted on 4 Oct 2016HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.Tree stem reconstruction from terrestrial laser scanner point cloud using Houghtransform and open active contours Joris Ravaglia1,2, Alexandra Bac1, Richard A. Fournier2 1LSIS – Université d'Aix-Marseille - France2 Centre d’Applications et de Recherches en Télédétection (CARTEL), Département de géomatique appliquée,Université de Sherbrooke, Sherbrooke (QC) CANADAHighlights: We introduce an innovative methodology for reconstructing tree stems from terrestrialLiDAR data. The method uses a multi-scale combination of an original Hough transform and activecontours. Tests were conducted on both simulated data and real LiDAR data. Even though no formalevaluation was completed yet, the procedure seems promising.Key words: terrestrial LiDAR, stem reconstruction, Hough transform, open active contours IntroductionTree structural attributes are crucial to forest inventories measurements. Indeed, structural attributes such asdiameter at breast height (DBH) or stem taper are used in a wide range of applications. For example, they areemployed in forest monitoring, plant growth models and allometric models [1]. However, the measurement ofsome of these attributes is often limited in the context of non-destructive field work, and their precision can alsobe limited by measurement tools and operational constraints. Thus, terrestrial laser scanners (TLS) have beenintroduced as a measuring tool to expand the number and the quality of the structural attributes available inforest inventories. TLS instruments provide a precise 3D point cloud characterizing the surrounding environmentof a forest plot from which tree attributes can be estimated in an non-destructive and objective way. Assuming that tree stem cross-sections are circular, DBH and stem taper can be estimated using patternrecognition algorithms applied to point clouds. Point cloud processing methods such as circle fitting, cylinderfitting or cone fitting are indeed well-suited for reconstructing tree stem [2]. Hough transform is another way ofretrieving DBH from point clouds [3]. Traditional Hough transform has shown a stem detection rate above 90%and an average DBH estimation error of 1.7 cm when applied to natural forest environment [3]. The analysis ofthe Hough space obtained by Hough transform can be a hard task, hence, despite these positive results, shapefitting  has  become  the  main  method  to  reconstruct  tree  stems.  However,  Hough  transform,  as  a  patternrecognition tool, has demonstrated to be adapted for application in complex forest environment.In this study we aim at developing a novel methodology for tree stem reconstruction in order to estimateDBH and stem taper. This methodology is intended to be able to overcome some limitations of point cloudanalysis: occlusion, non-homogeneous sampling density and noise. To do so, we take advantage of the Houghtransform qualities and propose an innovative use of this pattern recognition tool.Material and methods We reconstruct tree stems as an ordered series of circles in the 3D space with continuous centre locations,orientations and radii, which we refer to as tuboid. We introduce a multi-resolution approach for extracting suchseries of circles from the TLS point cloud data. At each resolution, in a first step, a Hough space describingpotential circles in the data is created. In a second step we employ growing open active contours to extracttuboids from this space.We use a variant of the classical Hough transform adapted to circle detection based on normal direction ateach point of the data to obtain a discrete 4D Hough space (HS). A cell (x,y,z,r) of this space describes thenumber of data points belonging to the circle c(x,y,z,r). The Hough space is computed according to the followingproperty: the normal direction at a point on a circle passes through the centre of this circle. This property allowscreating the corresponding Hough space using fast ray tracing algorithms.A tuboid in this HS is represented as a curve. Since the elements of the HS represent the number of pointsbelonging to a circle, the elements of highest value tends to be an accurate reconstruction of the data. Thus, weintend to extract curves passing through high value elements. To do so, we use open active contours. We define acurve energy which is minimum when the curve passes through these high value elements with a low curvaturein order to reduce noise effect and obtain a smooth taper. Classical active contours need an initialisation of theirposition. Such a general initialisation along a stem can be a delicate issue. Instead, we initialise small curves atthe local maximum of the HS and let the active contours grow along the elements of high value by adding agrowing term to the curves.The  precision  of  results  is  related  to  the  Hough  space  resolution.  However,  the  computation  of  highresolution Hough spaces for 20 m × 20 m forest plots is time consuming and needs a lot of computer resources.This is why we propose a multi-resolution scheme. Starting from a coarse Hough space,  coarse tuboids areextracted. Then, a precise sub-Hough space is computed with finer resolution.ResultsUp to now, the  methodology was  tested  on both simulated  and  real  datasets.  The first  test  dataset  is  asimulated point cloud containing a set of 48 cylinders with a constant radius (50 cm) with various orientationsand increasing noise (from 0% to 10% of the radius). The second dataset is a single-scan acquired point cloud ina real forest environment using the Faro Focus 3D TLS (Fig. 1).Normal directions have been estimated by localplane fitting for both dataset.  The 48 cylinders of the first  data set were successfully  reconstructed with anaverage error of 1.07 cm. This illustrates the robustness of the HT to noise (Fig. 1). In the second data set fromreal data, tree stem from the ground to the centre of the crown.DiscussionThe method we introduce is based on a combination of two powerful pattern recognition tools. Our variant ofthe classic  HT needs an estimation of  the normal at  each  points  of  the cloud.  Thus,  the results  are  highlydependent  on  the  quality  of  the  normal  estimation  procedure.  However,  this  HT  has  a  low  algorithmiccomplexity due to to the availability of the normal information: the number of necessary operations is linearlyrelated with n, the number of points in the cloud. Moreover, HS elements contain only local information, and HSanalysis with active contours is based on the detection of local high values, making the entire methodology ableto deal with non-homogeneous sampling density.The use of growing open active contours ensures a continuity of location, orientation and radii inside eachtuboid following what is observed for tree stem and branches. These growing active contours also avoid the issueof  initialisation and  the need  of  a priori information about  the  location  of  tree  stems.  Finally, through theminimization of the internal energy of curves and the addition of a growing term to the active contours, missinginformation in occluded areas may be coherently estimated according to the surrounding non-occluded zones.ConclusionWe proposed a novel methodology to reconstruct tree stems from LiDAR data collected in forest plots. Thismethodology aims at overcoming problems related to the use of TLS in forest environment such as occlusion,non-homogeneous sampling densities and noise. By combining active contours with the Hough transform, wetake advantage of their respective qualities in an innovative novel way. This treatment differs from the iterativeshape fitting procedures since stem reconstruction is embedded in a single object recognition: a tuboid.References[1]Schneider, R., et al., Calibrating jack pine allometric relationships with simultaneous regressions. Canadianjournal of forest research, 38(10), 2566–2578[2] Dassot, M., Constant, T., & Fournier, M. (2011). The use of terrestrial LiDAR technology in forest science:application fields, benefits and challenges. Annals of Forest Science, 68(5), 959–974.[3]  Simonse, M., Aschoff, T., Spiecker, H., & Thies, M. (2003). Automatic determination of forest inventoryparameters using terrestrial laser scanning. In Proceedings of the scandlaser scientific workshop on airbornelaser scanning of forests, 2003, 252-258.Figure 1 : (a) simulated point cloud : cylinders with increasing orientation angle and noise, (b) reconstructionwith Hough transform (circles are coloured according to their value in the HS), (c) laser scanned tree stem andreconstruction as tuboid

Tree stem reconstruction from terrestrial laser scanner point cloud using Hough transform and open active contours

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Tree stem reconstruction from terrestrial laser scanner point cloud using Hough transform and open active contours

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