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arXiv Computer Vision By Denis M. Akola, David F. Fouhey

Zero-Shot Novel Depth Synthesis Using 3D Foundation Models Scene Representations

Read the original on arXiv Computer Vision →

The paper introduces Z3D, a method that leverages internal representations from 3D Foundation Models (3DFMs) to perform zero‑shot novel depth synthesis. By decoding hidden surfaces and applying latent diffusion on 3DFM representations, Z3D can estimate realistic depth maps for unseen views across multiple datasets. This demonstrates that 3DFMs capture extensive general knowledge about 3D scenes, enabling accurate reconstruction without additional training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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Zero-Shot Novel Depth Synthesis Using 3D Foundation Models Scene Representations

The paper explores how 3D Foundation Models (3DFMs) like VGGT can be leveraged for zero‑shot depth synthesis. By decoding hidden surfaces from the models’ internal representations, the authors introduce Z3D, a method that uses latent diffusion on 3DFM representations to estimate pointmaps in unseen views. Experiments demonstrate that Z3D can generate realistic depth maps across multiple datasets.

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