E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918413881835520 |
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| author | Zhao, Qitao Tan, Hao Wang, Qianqian Bi, Sai Zhang, Kai Sunkavalli, Kalyan Tulsiani, Shubham Jiang, Hanwen |
| author_facet | Zhao, Qitao Tan, Hao Wang, Qianqian Bi, Sai Zhang, Kai Sunkavalli, Kalyan Tulsiani, Shubham Jiang, Hanwen |
| contents | Self-supervised pre-training has driven rapid progress in foundation models for language, 2D images, and video, yet remains largely unexplored for learning 3D-aware representations from multi-view images. In this paper, we present E-RayZer, a self-supervised 3D vision model that learns geometrically grounded representations directly from unlabeled images. Unlike prior self-supervised methods such as RayZer, which infer 3D indirectly through latent-space view synthesis, E-RayZer operates directly in 3D space, performing self-supervised 3D reconstruction with Explicit geometry. This formulation eliminates shortcut solutions and yields representations that are 3D-aware. To ensure convergence and scalability, we introduce a fine-grained learning curriculum that organizes training from easy to hard samples and harmonizes heterogeneous data sources without any supervision. Experiments show that E-RayZer significantly outperforms RayZer on pose estimation and matches or sometimes surpasses fully supervised reconstruction models such as VGGT. Furthermore, its learned representations outperform leading visual pre-training models (e.g., DINOv3, CroCo v2, VideoMAE V2, and RayZer) on 3D downstream tasks, establishing E-RayZer as a promising paradigm for spatial visual pre-training. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_10950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training Zhao, Qitao Tan, Hao Wang, Qianqian Bi, Sai Zhang, Kai Sunkavalli, Kalyan Tulsiani, Shubham Jiang, Hanwen Computer Vision and Pattern Recognition Self-supervised pre-training has driven rapid progress in foundation models for language, 2D images, and video, yet remains largely unexplored for learning 3D-aware representations from multi-view images. In this paper, we present E-RayZer, a self-supervised 3D vision model that learns geometrically grounded representations directly from unlabeled images. Unlike prior self-supervised methods such as RayZer, which infer 3D indirectly through latent-space view synthesis, E-RayZer operates directly in 3D space, performing self-supervised 3D reconstruction with Explicit geometry. This formulation eliminates shortcut solutions and yields representations that are 3D-aware. To ensure convergence and scalability, we introduce a fine-grained learning curriculum that organizes training from easy to hard samples and harmonizes heterogeneous data sources without any supervision. Experiments show that E-RayZer significantly outperforms RayZer on pose estimation and matches or sometimes surpasses fully supervised reconstruction models such as VGGT. Furthermore, its learned representations outperform leading visual pre-training models (e.g., DINOv3, CroCo v2, VideoMAE V2, and RayZer) on 3D downstream tasks, establishing E-RayZer as a promising paradigm for spatial visual pre-training. |
| title | E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.10950 |