Image-Based Modeling of Plants and Trees

Image-Based Modeling of Plants and Trees

Sing Bang Kang, Long Quan
ISBN: 9781608452446 | PDF ISBN: 9781608452453
Copyright © 2009 | 83 Pages | Publication Date: 01/01/2009

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Plants and trees are among the most complex natural objects. Much work has been done attempting to model them, with varying degrees of success. In this book, we review the various approaches in computer graphics, which we categorize as rule-based, image-based, and sketch-based methods.

We describe our approaches for modeling plants and trees using images. Image-based approaches have the distinct advantage that the resulting model inherits the realistic shape and complexity of a real plant or tree. We use different techniques for modeling plants (with relatively large leaves) and trees (with relatively small leaves).With plants, we model each leaf from images, while for trees, the leaves are only approximated due to their small size and large number. Both techniques start with the same initial step of structure from motion on multiple images of the plant or tree that is to be modeled.

For our plant modeling system, because we need to model the individual leaves, these leaves need to be segmented out from the images. We designed our plant modeling system to be interactive, automating the process of shape recovery while relying on the user to provide simple hints on segmentation. Segmentation is performed in both image and 3D spaces, allowing the user to easily visualize its effect immediately. Using the segmented image and 3D data, the geometry of each leaf is then automatically recovered from the multiple views by fitting a deformable leaf model. Our system also allows the user to easily reconstruct branches in a similar manner.

To model trees, because of the large leaf count, small image footprint, and widespread occlusions, we do not model the leaves exactly as we do for plants. Instead, we populate the tree with leaf replicas from segmented source images to reconstruct the overall tree shape. In addition, we use the shape patterns of visible branches to predict those of obscured branches. As a result, we are able to design our tree modeling system so as to minimize user intervention.

We also handle the special case of modeling a tree from only a single image. Here, the user is required to draw strokes on the image to indicate the tree crown (so that the leaf region is approximately known) and to refine the recovery of branches. As before, we concatenate the shape patterns from a library to generate the 3D shape.

To substantiate the effectiveness of our systems, we show realistic reconstructions of a variety of plants and trees from images. Finally, we offer our thoughts on improving our systems and on the remaining challenges associated with plant and tree modeling.

Table of Contents

Review of Plant and Tree Modeling Techniques
Image-Based Technique for Modeling Plants
Image-Based Technique for Modeling Trees
Single Image Tree Modeling
Summary and Concluding Remarks

About the Author(s)

Sing Bang Kang, Microsoft Research
Sing Bing Kang received his Ph.D. in robotics from Carnegie Mellon University, Pittsburgh in 1994. He is Principal Researcher at Microsoft Corporation and adjunct faculty member of the School of Interactive Computing at Georgia Tech. His interests are image-based modeling as well as image and video enhancement. Sing Bing has co-edited two books in computer vision ("Panoramic Vision" and "Emerging Topics in Computer Vision"), and coauthored a book on image-based rendering. He has served as area chair and member of technical committee for the three major computer vision conferences, namely International Conference on Computer Vision (ICCV), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), and European Conference on Computer Vision (ECCV). He was program co-chair for CVPR 2009, and has served as papers committee member for SIGGRAPH 2007, SIGGRAPH Asia 2008, and SIGGRAPH 2009. Sing Bing is currently an Associate Editor for IEEE TPAMI and Associate Editor-in-Chief for IPSJ Transactions on Computer Vision and Applications.

Long Quan, The Hong Kong University of Science and Technology
Long Quan is a Professor of the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology. He received his Ph.D. in Computer Science from INPL, France, in 1989. Before moving back to Hong Kong in 2001, he has been a French CNRS senior research scientist at INRIA in Grenoble. His research interests are focused on 3D reconstruction, structure from motion, vision geometry, and image-based modeling. He has served as an Associate Editor of PAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence) and as a Regional Editor of IVC (Image and Vision Computing Journal). He is currently on the editorial board of IJCV (the International Journal of Computer Vision), ELCVIA (the Electronic Letters on Computer Vision and Image Analysis), MVA (Machine Vision and Applications) and Foundations and Trends in Computer Graphics and Vision. He has served as area chair for ICCV (International Conference on Computer Vision), ECCV (European Conference on Computer Vision), and CVPR (IEEE Computer Vision and Pattern Recognition) and ICPR (IAPR International Conference on Pattern Recognition). He was a Program Chair of ICPR 2006 Computer Vision and Image Analysis, and is a General Chair of ICCV 2011 in Barcelona.

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