Abstract
Studying growth and development of plants is of central importance in botany. Current quantitative are either limited to tedious and sparse manual measurements, or coarse image-based 2D measurements. Availability of cheap and portable 3D acquisition devices has the potential to automate this process and easily provide scientists with volumes of accurate data, at a scale much beyond the realms of existing methods. However, during their development, plants grow new parts (e.g., vegetative buds) and bifurcate to different components --- violating the central incompressibility assumption made by existing acquisition algorithms, which makes these algorithms unsuited for analyzing growth. We introduce a framework to study plant growth, particularly focusing on accurate localization and tracking topological events like budding and bifurcation. This is achieved by a novel forward-backward analysis, wherein we track robustly detected plant components back in time to ensure correct spatio-temporal event detection using a locally adapting threshold. We evaluate our approach on several groups of time lapse scans, often ranging from days to weeks, on a diverse set of plant species and use the results to animate static virtual plants or directly attach them to physical simulators.
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- Ahmed, N., Theobalt, C., Dobrev, P., Seidel, H.-P., and Thrun, S. 2008. Robust fusion of dynamic shape and normal capture for high-quality reconstruction of time-varying geometry. In IEEE CVPR, 1--8.Google Scholar
- Akhter, I., Simon, T., Khan, S., Matthews, I., and Sheikh, Y. 2012. Bilinear spatiotemporal basis models. ACM TOG 31, 2, 17:1--17:12. Google ScholarDigital Library
- Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., and Silva, C. T. 2001. Point set surfaces. In IEEE Vis, VIS '01, 21--28. Google ScholarDigital Library
- Beeler, T., Hahn, F., Bradley, D., Bickel, B., Beardsley, P., Gotsman, C., Sumner, R. W., and Gross, M. 2011. High-quality passive facial performance capture using anchor frames. ACM TOG 30, 75:1--75:10. Google ScholarDigital Library
- Bojsen-Hansen, M., Li, H., and Wojtan, C. 2012. Tracking surfaces with evolving topology. ACM TOG 31, 4, 53:1--53:10. Google ScholarDigital Library
- Boykov, Y., and Kolmogorov, V. 2004. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE TPAMI 26, 9, 1124--1137. Google ScholarDigital Library
- Boykov, Y., Veksler, O., and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE TPAMI 23, 11, 1222--1239. Google ScholarDigital Library
- Bradley, D., Popa, T., Sheffer, A., Heidrich, W., and Boubekeur, T. 2008. Markerless garment capture. ACM TOG 27, 3, 99:1--99:9. Google ScholarDigital Library
- Brendel, W., and Todorovic, S. 2011. Learning spatiotemporal graphs of human activities. In IEEE ICCV, 778--785. Google ScholarDigital Library
- Chang, W., and Zwicker, M. 2009. Range scan registration using reduced deformable models. CGF 28, 2, 447--456.Google ScholarCross Ref
- Chang, W., and Zwicker, M. 2011. Global registration of dynamic range scans for articulated model reconstruction. ACM TOG 30, 3, 26:1--26:15. Google ScholarDigital Library
- Chen, X., and Laux, T. 2012. Plant development - a snapshot in 2012. Current Opinion in Plant Biology 15, 1, 1--3.Google ScholarCross Ref
- Curless, B. 1999. From range scans to 3d models. Proc. of SIGGRAPH 33, 4 (Nov.), 38--41. Google ScholarDigital Library
- de Aguiar, E., Stoll, C., Theobalt, C., Ahmed, N., Seidel, H.-P., and Thrun, S. 2008. Performance capture from sparse multi-view video. ACM TOG 27, 3, 98:1--98:10. Google ScholarDigital Library
- Fernandez, R., Das, P., Mirabet, V., Moscardi, E., Traas, J., Verdeil, J.-L., Malandain, G., and Godin, C. 2010. Imaging plant growth in 4d: robust tissue reconstruction and lineaging at cell resolution. Nature Methods 7, 7, 547--553.Google ScholarCross Ref
- Gaur, U., Zhu, Y., Song, B., and Roy-Chowdhury, A. 2011. A "string of feature graphs" model for recognition of complex activities in natural videos. In IEEE ICCV, 2595--2602. Google ScholarDigital Library
- Huang, H., Li, D., Zhang, H., Ascher, U., and Cohen-Or, D. 2009. Consolidation of unorganized point clouds for surface reconstruction. ACM TOG 28, 5, 176:1--176:7. Google ScholarDigital Library
- Huang, H., Wu, S., Cohen-Or, D., Gong, M., Zhang, H., Li, G., and Chen, B. 2013. L1-medial skeleton of point cloud. ACM TOG 32. Google ScholarDigital Library
- Kalal, Z., Mikolajczyk, K., and Matas, J. 2010. Forward-backward error: Automatic detection of tracking failures. In Int. Conf. on Pattern Recognition, 2756--2759. Google ScholarDigital Library
- Kazhdan, M., Bolitho, M., and Hoppe, H. 2006. Poisson surface reconstruction. In Proc. SGP, 61--70. Google ScholarDigital Library
- Kevin, T., Fei-Fei, L., and Koller, D. 2012. Learning latent temporal structure for complex event detection. In IEEE CVPR.Google Scholar
- Kolmogorov, V., and Zabih, R. 2004. What energy functions can be minimized via graph cuts? IEEE TPAMI 26, 2, 147--159. Google ScholarDigital Library
- Li, C., Deussen, O., Song, Y.-Z., Willis, P., and Hall, P. 2011. Modeling and generating moving trees from video. ACM TOG 30, 6, 127:1--127:12. Google ScholarDigital Library
- Li, H., Luo, L., Vlasic, D., Peers, P., Popović, J., Pauly, M., and Rusinkiewicz, S. 2012. Temporally coherent completion of dynamic shapes. ACM TOG 31, 1, 2:1--2:11. Google ScholarDigital Library
- Liao, M., Zhang, Q., Wang, H., Yang, R., and Gong, M. 2009. Modeling deformable objects from a single depth camera. In IEEE ICCV, 167--174.Google Scholar
- Livny, Y., Yan, F., Olson, M., Chen, B., Zhang, H., and El-Sana, J. 2010. Automatic reconstruction of tree skeletal structures from point clouds. ACM TOG 29, 6, 151:1--151:8. Google ScholarDigital Library
- Lu, C., Chelikani, S., Jaffray, D., Milosevic, M., Staib, L., and Duncan, J. 2012. Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy. IEEE Trans. on Medical Imaging 31, 6, 1213--1227.Google ScholarCross Ref
- Mitra, N. J., Flöry, S., Ovsjanikov, M., Gelfand, N., Guibas, L., and Pottmann, H. 2007. Dynamic geometry registration. In Proc. SGP, 173--182. Google ScholarDigital Library
- Mündermann, L., Erasmus, Y., Lane, B., Coen, E., and Prusinkiewicz, P. 2005. Quantitative modeling of arabidopsis development. Plant physiology 139, 2, 960--968.Google Scholar
- Neubert, B., Franken, T., and Deussen, O. 2007. Approximate image-based tree-modeling using particle flows. ACM TOG 26, 3. Google ScholarDigital Library
- Pirk, S., Niese, T., Deussen, O., and Neubert, B. 2012. Capturing and animating the morphogenesis of polygonal tree models. ACM TOG 31, 6 (Nov.), 169:1--169:10. Google ScholarDigital Library
- Pirk, S., Stava, O., Kratt, J., Said, M. A. M., Neubert, B., Měch, R., Benes, B., and Deussen, O. 2012. Plastic trees: interactive self-adapting botanical tree models. ACM TOG 31, 4 (July), 50:1--50:10. Google ScholarDigital Library
- Pirsiavash, H., and Ramanan, D. 2012. Detecting activities of daily living in first-person camera views. In IEEE CVPR. Google ScholarDigital Library
- Pons-Moll, G., Baak, A., Gall, J., Leal-Taixe, L., Muller, M., Seidel, H.-P., and Rosenhahn, B. 2011. Outdoor human motion capture using inverse kinematics and von mises-fisher sampling. In IEEE ICCV, 1243--1250. Google ScholarDigital Library
- Popa, T., South-Dickinson, I., Bradley, D., Sheffer, A., and Heidrich, W. 2010. Globally consistent space-time reconstruction. CGF 29, 5, 1633--1642.Google ScholarCross Ref
- Prusinkiewicz, P., and Lindenmayer, A. 1996. The algorithmic beauty of plants. Google ScholarDigital Library
- Prusinkiewicz, P., and Runions, A. 2012. Computational models of plant development and form. New Phytologist 193, 3, 549--569.Google ScholarCross Ref
- Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., and Kang, S. B. 2006. Image-based plant modeling. ACM TOG 25, 3, 599--604. Google ScholarDigital Library
- Rozenberg, G., and Salomaa, A. 1980. Mathematical Theory of L Systems. Academic Press, Inc., Orlando, FL, USA. Google ScholarDigital Library
- Sharf, A., Alcantara, D. A., Lewiner, T., Greif, C., Sheffer, A., Amenta, N., and Cohen-Or, D. 2008. Space-time surface reconstruction using incompressible flow. ACM TOG 27, 5, 110:1--110:10. Google ScholarDigital Library
- Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., and Blake, A. 2011. Real-time human pose recognition in parts from single depth images. In IEEE CVPR, 1297--1304. Google ScholarDigital Library
- Song, Z., and Chung, R. 2008. Use of lcd panel for calibrating structured-light-based range sensing system. IEEE Trans. on Instrumentation and Measurement 57, 11, 2623--2630.Google ScholarCross Ref
- Song, Z., Chung, R., and Zhang, X.-T. 2013. An accurate and robust strip-edge-based structured light means for shiny surface micromeasurement in 3D. IEEE Trans. on Industrial Electronics 60, 3, 1023--1032.Google ScholarCross Ref
- Tevs, A., Berner, A., Wand, M., Ihrke, I., Bokeloh, M., Kerber, J., and Seidel, H.-P. 2012. Animation cartographyintrinsic reconstruction of shape and motion. ACM TOG 31, 2, 12:1--12:15. Google ScholarDigital Library
- Thrun, S., and Montemerlo, M. 2005. The graphslam algorithm with applications to large-scale mapping of urban structures. Int. J. on Robotics Research 25, 5/6, 403--430. Google ScholarDigital Library
- Vlasic, D., Baran, I., Matusik, W., and Popović, J. 2008. Articulated mesh animation from multi-view silhouettes. ACM TOG 27, 3, 97:1--97:9. Google ScholarDigital Library
- Wand, M., Adams, B., Ovsjanikov, M., Berner, A., Bokeloh, M., Jenke, P., Guibas, L., Seidel, H.-P., and Schilling, A. 2009. Efficient reconstruction of nonrigid shape and motion from real-time 3d scanner data. ACM TOG 28, 2, 15:1--15:15. Google ScholarDigital Library
- Xu, H., Gossett, N., and Chen, B. 2007. Knowledge and heuristic-based modeling of laser-scanned trees. ACM TOG 26, 4. Google ScholarDigital Library
- Yamazaki, S., Narasimhan, S. G., Baker, S., and Kanade, T. 2007. Coplanar shadowgrams for acquiring visual hulls of intricate objects. In IEEE ICCV, 1--8.Google Scholar
- Yamazaki, S., Narasimhan, S. G., Baker, S., and Kanade, T. 2009. The theory and practice of coplanar shadowgram imaging for acquiring visual hulls of intricate objects. IJCV 81, 3, 259--280. Google ScholarDigital Library
- Zhao, Y., and Barbič, J. 2013. Interactive authoring of simulation-ready plants. ACM TOG 32, 4. Google ScholarDigital Library
- Zheng, Q., Sharf, A., Tagliasacchi, A., Chen, B., Zhang, H., Sheffer, A., and Cohen-Or, D. 2010. Consensus skeleton for non-rigid space-time registration. CGF 29, 635--644.Google ScholarCross Ref
Index Terms
- Analyzing growing plants from 4D point cloud data
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