My research interests lie in the area of 3D computer vision, 3D foundation models, 3D reconstruction, multi-view 3D reconstruction and surface reconstruction from point clouds.
UDiFF is a 3D diffusion model for unsigned distance fields (UDFs) which is capable to generate textured 3D shapes with open surfaces from text conditions or unconditionally.
We present CAP-UDF to represent shapes and scenes with arbitrary architecture by learning a Consistency-Aware unsigned distance function Progressively.
We present 3D-OAE, a novel self-supervised point cloud representation learning framework which is highly efficient and can be further transferred to various downstream tasks.
We present GeoDream, a 3D generation method that incorporates explicit generalized 3D priors with 2D diffusion priors to enhance the capability of obtaining unambiguous 3D consistent geometric structures without sacrificing diversity or fidelity.
We present Uni3D, a unified and scalable 3D pretraining framework for large-scale 3D representation learning, and explore its limits at the scale of one billion parameters.
We present CAP-UDF to represent shapes and scenes with arbitrary architecture by learning a Consistency-Aware unsigned distance function Progressively.