Depth Anything 3: Recovering the Visual Space from Any Views ( Code , Model available). lot of examples on project page.

Project page: https://depth-anything-3.github.io/
Paper: https://arxiv.org/pdf/2511.10647
Demo: https://huggingface.co/spaces/depth-anything/depth-anything-3
Github: https://github.com/ByteDance-Seed/depth-anything-3

Depth Anything 3, a single transformer model trained exclusively for joint any-view depth and pose estimation via a specially chosen ray representation. Depth Anything 3 reconstructs the visual space, producing consistent depth and ray maps that can be fused into accurate point clouds, resulting in high-fidelity 3D Gaussians and geometry. It significantly outperforms VGGT in multi-view geometry and pose accuracy; with monocular inputs, it also surpasses Depth Anything 2 while matching its detail and robustness.