From None to All: Self-Supervised 3D Reconstruction via Novel View Synthesis

• URL: http://arxiv.org/abs/2603.27455v1
• Date: Sun, 29 Mar 2026 00:28:38 GMT
• Title: From None to All: Self-Supervised 3D Reconstruction via Novel View Synthesis
• Abstract summary: We introduce NAS3R, a self-supervised feed-forward framework that jointly learns explicit 3D geometry and camera parameters.<n>During training, NAS3R reconstructs 3D Gaussians from uncalibrated and unposed context views.<n>NAS3R renders target views using its self-predicted camera parameters, enabling self-supervised training from 2D photometric supervision.
• Score: 18.24499102151468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce NAS3R, a self-supervised feed-forward framework that jointly learns explicit 3D geometry and camera parameters with no ground-truth annotations and no pretrained priors. During training, NAS3R reconstructs 3D Gaussians from uncalibrated and unposed context views and renders target views using its self-predicted camera parameters, enabling self-supervised training from 2D photometric supervision. To ensure stable convergence, NAS3R integrates reconstruction and camera prediction within a shared transformer backbone regulated by masked attention, and adopts a depth-based Gaussian formulation that facilitates well-conditioned optimization. The framework is compatible with state-of-the-art supervised 3D reconstruction architectures and can incorporate pretrained priors or intrinsic information when available. Extensive experiments show that NAS3R achieves superior results to other self-supervised methods, establishing a scalable and geometry-aware paradigm for 3D reconstruction from unconstrained data. Code and models are publicly available at https://ranrhuang.github.io/nas3r/.

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