UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal
  Gaussian Splatting
Authors: name: Ziyi Wang; name: Yanran Zhang; name: Jie Zhou; name: Jiwen Lu
Published: 2025-06-11 17:23:21

Abstract: The scale diversity of point cloud data presents significant challenges in
developing unified representation learning techniques for 3D vision. Currently,
there are few unified 3D models, and no existing pre-training method is equally
effective for both object- and scene-level point clouds. In this paper, we
introduce UniPre3D, the first unified pre-training method that can be
seamlessly applied to point clouds of any scale and 3D models of any
architecture. Our approach predicts Gaussian primitives as the pre-training
task and employs differentiable Gaussian splatting to render images, enabling
precise pixel-level supervision and end-to-end optimization. To further
regulate the complexity of the pre-training task and direct the model's focus
toward geometric structures, we integrate 2D features from pre-trained image
models to incorporate well-established texture knowledge. We validate the
universal effectiveness of our proposed method through extensive experiments
across a variety of object- and scene-level tasks, using diverse point cloud
models as backbones. Code is available at https://github.com/wangzy22/UniPre3D.