We present a lobe-based tree representation for modeling trees. The new representation is based on the observation that the tree's foliage details can be abstracted into canonical geometry structures, termed lobe-textures. We introduce techniques to (i) approximate the geometry of given tree data and encode it into a lobe-based representation, (ii) decode the representation and synthesize a fully detailed tree model that visually resembles the input. The encoded tree serves as a light intermediate representation, which facilitates efficient storage and transmission of massive amounts of trees, e.g., from a server to clients for interactive applications in urban environments. The method is evaluated by both reconstructing laser scanned trees (given as point sets) as well as re-representing existing tree models (given as polygons).

We propose a novel deep learning-based method, called mesh superresolution, to enrich low-resolution (LR) cloth meshes with wrinkles. A pair of low and high-resolution (HR) meshes are simulated, with the simulation of the HR mesh tracks with that of the LR mesh. The frame data are converted into geometry images and used as a training data set. A residual network, called SR residual network, is employed to train an image synthesizer that superresolves an LR image into an HR one. Once the HR image is converted back to an HR mesh, it is abundant in wrinkles compared with its coarse counterpart. The synthesizing is very efficient and is 24× faster than a full HR simulation. We demonstrate the performances of mesh superresolution with various simulation scenes.

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