E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training

• URL: http://arxiv.org/abs/2512.10950v1
• Date: Thu, 11 Dec 2025 18:59:53 GMT
• Title: E-RayZer: Self-supervised 3D Reconstruction as Spatial Visual Pre-training
• Authors: Qitao Zhao, Hao Tan, Qianqian Wang, Sai Bi, Kai Zhang, Kalyan Sunkavalli, Shubham Tulsiani, Hanwen Jiang,
• Abstract summary: We present E-RayZer, a self-supervised large 3D Vision model that learns truly 3D-aware representations directly from unlabeled images.<n>E-RayZer operates directly in 3D space, performing self-supervised 3D reconstruction with Explicit geometry.
• Score: 55.61379509038588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Self-supervised pre-training has revolutionized foundation models for languages, individual 2D images and videos, but remains largely unexplored for learning 3D-aware representations from multi-view images. In this paper, we present E-RayZer, a self-supervised large 3D Vision model that learns truly 3D-aware representations directly from unlabeled images. Unlike prior self-supervised methods such as RayZer that 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 geometrically grounded. To ensure convergence and scalability, we introduce a novel fine-grained learning curriculum that organizes training from easy to hard samples and harmonizes heterogeneous data sources in an entirely unsupervised manner. Experiments demonstrate that E-RayZer significantly outperforms RayZer on pose estimation, 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) when transferring to 3D downstream tasks, establishing E-RayZer as a new paradigm for 3D-aware visual pre-training.

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