2d-to-3d feature distillation 13d shape understanding 1feedforward networks 1mesh analysis 1unsupervised 3d learning 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CV2026
MeshFM: 2D Features Are All You Need for 3D Shape Understanding
Jinfan Zhou, Richard Liu, Itai Lang +1
MeshFM is a feedforward framework that learns 3D shape features by distilling 2D features from visual foundation models using a two‑stage training process that requires no 3D annot…
cs.CV2026
Deep Feature Deformation Weights
Richard Liu, Itai Lang, Rana Hanocka
Handle-based mesh deformation is a classic paradigm in computer graphics which enables intuitive edits from sparse controls. Classical techniques are fast and precise, but require…
cs.GR2025
WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction
Richard Liu, Daniel Fu, Noah Tan +2
In this work we present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that…