Image-based Tree Branches Recovery

Image-based Tree Branches Recovery

Image-based Tree Branch Recovery Yilin Wang Rule-based using a small set of generative rules or grammar to create branches and leaves

Image-based reconstructing the tree directly from image samples Tree modeling A

L-system consists of an axiom and productions Example L-system P. Prusinkiewicz, M. James, and R. Mech. Synthetic topiary. In SIGGRAPH 94: Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pages 351358, New York, NY, USA, 1994.

ACM. Sample tree structures generated using L-system P. Tan, G. Zeng, J. Wang, S. B. Kang, and L. Quan. Image-based tree modeling. In SIGGRAPH 07: ACM SIGGRAPH 2007 papers, page 87, New York, NY, USA, 2007. ACM.

Image-based tree modeling P. Tan, G. Zeng, J. Wang, S. B. Kang, and L. Quan. Image-based tree modeling. In SIGGRAPH 07: ACM SIGGRAPH 2007 papers, page 87, New York, NY, USA, 2007. ACM. Bare tree example Problem

it is not easy to generate complete tree models from just 3D points because of the difficulties in determining what is missing and in filling the missing information Can we extract more useful information from images to refine the branch reconstruction?

Motivation Final goal To recover the tree branches from images, and to make the procedure as automatic as possible

Subtasks 3D structure generation from source images Reconstruction of trunk and visible branches Recovery of occlude branches Potential improvements

Using segmentations and gradient maps to refine branches, e.g. estimate length, density, and direction of the branch My project Thank you!

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