I am a passionate researcher who seeks creative solutions to new problems and innovative applications. My research is focused on developing artificial intelligence for geometry processing, spanning the fields of computer vision, computer graphics, and machine learning.
We apply a low frequency perturbation in the spectral shape domain to alter the reconstruction by a victim mesh autoencoder to a desired output geometry.
We use pure correspondence learning and direct refinement optimization to predict a highly accurate scene flow while using a fraction of the training data.
We build a procedural program with an interpretable parameter space to recover a high quality and easily manipulated mesh from a point cloud or a sketch input.
We use latent similarity and the point coordinates themselves to construct one point cloud by the other and achive an accurate dense matching with a small training data amount and without any correspondence supervision.