Abstract
The recent availability of low-cost RGB-D sensors and the maturity of machine vision algorithms makes shape-based parametric modeling of 3D objects in natural environments more practical than ever before. In this paper, we investigate the use of RGB-D based modeling of natural objects using RGB-D sensors and a combination of volumetric 3D reconstruction and parametric shape modeling. We apply the general method to the specific case of detecting and modeling quadric objects, with the ellipsoid shape of a pineapple as a special case, in cluttered agricultural environments, towards applications in fruit health monitoring and crop yield prediction. Our method estimates the camera trajectory then performs volumetric reconstruction of the scene. Next, we detect fruit and segment out point clouds that belong to fruit regions. We use two novel methods for robust estimation of a parametric shape model from the dense point cloud: (i) MSAC-based robust fitting of an ellipsoid to the 3D-point cloud, and (ii) nonlinear least squares minimization of dense SIFT (scale invariant feature transform) descriptor distances between fruit pixels in corresponding frames. We compare our shape modeling methods with a baseline direct ellipsoid estimation method. We find that model-based point clouds show a clear advantage in parametric shape modeling and that our parametric shape modeling methods are more robust and better able to estimate the size, shape, and volume of pineapple fruit than is the baseline direct method.
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Chaivivatrakul, S., Moonrinta, J., Dailey, M.N.: Towards automated crop yield estimation: detection and 3D reconstruction of pineapples in video sequences. In: International Conference on Computer Vision Theory and Applications (2010)
Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1996, pp. 303–312. ACM, New York (1996)
David Eberly: Distance from a Point to an Ellipsoid (2008). http://www.geometrictools.com/
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014)
Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: IEEE International Conference on Computer Vision (ICCV). Sydney, Australia, December 2013
Forster, C., Pizzoli, M., Scaramuzza, D.: Svo: fast semi-direct monocular visual odometry. In: Int. Conf. on Robotics and Automation, pp. 15–22 (2014)
Kerl, C., Sturm, J., Cremers, D.: Dense visual SLAM for RGB-D cameras. In: Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), pp. 2100–2106 (2013)
Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), May 2013
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proc. Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2007), Nara, Japan, November 2007
Li, Q., Griffiths, J.G.: Least squares ellipsoid specific fitting. In: Proceedings of the Geometric Modeling and Processing, 2004, pp. 335–340. IEEE (2004)
Moonrinta, J., Chaivivatrakul, S., Dailey, M.N., Ekpanyapong, M.: Fruit detection, tracking, and 3d reconstruction for crop mapping and yield estimation. In: IEEE International Conference on Control, Automation, Robotics and Vision (2010)
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality. ISMAR 2011, pp. 127–136. IEEE Computer Society, Washington, DC (2011)
Qureshi, W.S., Satoh, S., Dailey, M.N., Ekpanyapong, M.: Dense segmentation of textured fruits in video sequences. In: 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014), pp. 441–447 (2014)
Sarris, N., Strintzis, M.G.: 3D modeling and animation: Synthesis and analysis techniques for the human body. IGI Global (2005)
Shamir, A.: A survey on mesh segmentation techniques. In: Computer Graphics Forum, vol. 27, pp. 1539–1556. Wiley Online Library (2008)
Steinbruecker, F., Kerl, C., Sturm, J., Cremers, D.: Large-scale multi-resolution surface reconstruction from RGB-D sequences. In: IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, pp. 3264–3271 (2013)
Steinbruecker, F., Sturm, J., Cremers, D.: Volumetric 3D mapping in real-time on a CPU. In: Int. Conf. on Robotics and Automation, Hongkong, China, pp. 2021–2028 (2014)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: Proc. of the International Conference on Intelligent Robot Systems (IROS), October 2012
Torr, P.H., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78(1), 138–156 (2000)
TUM Computer Vision Group: Dense Visual Odometry and SLAM (2013). https://github.com/tum-vision/dvo_slam
TUM Computer Vision Group: Volumetric 3D Mapping in Real-Time on a CPU (2014). https://github.com/tum-vision/fastfusion
Varady, T., Martin, R.R., Cox, J.: Reverse engineering of geometric modelsan introduction. Computer-Aided Design 29(4), 255–268 (1997)
Wu, C.: Towards linear-time incremental structure from motion. In: 2013 International Conference on 3D Vision-3DV 2013, pp. 127–134. IEEE (2013)
Zuliani, M.: Ransac for dummies. Tech. rep., November 2008
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Nakaguro, Y., Qureshi, W.S., Dailey, M.N., Ekpanyapong, M., Bunnun, P., Tungpimolrut, K. (2015). Volumetric 3D Reconstruction and Parametric Shape Modeling from RGB-D Sequences. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_45
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DOI: https://doi.org/10.1007/978-3-319-23231-7_45
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