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

From None to All: Self-Supervised 3D Reconstruction via Novel View Synthesis arXiv:2603.27455v2 Announce Type: replace 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 s