UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal
  Gaussian Splatting
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.
684ae226dbd21a9cc27b107a
Ziyi Wang
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2025-06-13T07:40:18.063Z
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Ziyi Wang
user
LavenderLA
684ae226dbd21a9cc27b107b
Yanran Zhang
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2025-06-13T07:41:09.847Z
661cfae9a853782abad2a495
/avatars/39723a07bf9efed8278e009fe966d044.svg
Yanran Zhang
user
Yanran21
684ae226dbd21a9cc27b107c
Jie Zhou
684ae226dbd21a9cc27b107d
Jiwen Lu
UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal
  Gaussian Splatting
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.
UniPre3D is a unified pre-training method for 3D point clouds and models of any scale, using Gaussian primitives and 2D feature integration for effective performance across object and scene tasks.
point cloud 3D vision representation learning UniPre3D Gaussian primitives differentiable Gaussian splatting pixel-level supervision end-to-end optimization 2D features pre-trained image models geometric structures