WoodSeer: Predicting Inner Wood Defects from Outer Bark Features

The WoodSeer project is funded by the French ANR and coordinated by Thiéry Constant from INRA. Its purpose is to evaluate the use of machine learning to predict the interior distribution of defects inside roundwood from the external geometry of the wood.

3D Reconstruction

Work by Aishwarya Venkataramanan.

Within the project, several techniques for tree geometry reconstruction will be evaluated, from portable laser scanners to computer vision. The image below shows the reconstruction achieved using image sequences captured with a DJI Osmo pocket, processed with ColMap after our own pre-processing. A zoom on the reconstructed surface and its wireframe is also visible.

The reconstruction precision has been evaluated in comparison with 3D point couds acquired with a laser scanner:

Bark texture reconstruction and generation

From the generated bark, it is possible to extract a texture map, which corresponds to the unrolling of the bark:

From this texture, it is possible to train a GAN to generate artificial textures:

A demonstration of the resulting 3D reconstruction is available on https://dream.georgiatech-metz.fr/vrml-3d-viewer-woodseer/. The 3D visualization uses the X_ITE javascript framework, which we tested on Firefox, Chrome and Edge.

Interior structure prediction

Work by Mohamed Mejri

Predicting the interior density of the wood from the exterior geometry is one of the long-term objectives of the project. The image below show the output of a 3D network (UNet3D in this case but we tested others) from the wood surface geometry for toy examples. The images shows the iso-surface of density corresponding to simulated density, generated with ParaView. The blue surface is the ground truth whereas the red surface is built from the density predicted by the network.


Work by Salim Khazem

To be continued…

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