arXiv Computer Vision
Sep 3

Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depth

The paper introduces Epipolar Distillation (EpiDistill), a method that transfers scale‑aware geometric priors from multi‑view models to monocular depth foundation models using Rectified Stereo Tokens. By preserving epipolar attention patterns, the single‑view model maintains geometric consistency without needing multi‑view inputs during inference. Experiments show significant improvements in zero‑shot metric depth estimation on challenging datasets such as ETH3D and DIODE, and the approach consistently boosts performance of state‑of‑the‑art ViT‑based models like UniDepthV2 and DepthPro.

By Jung-Hee Kim, Xiaoming Liu
arXiv AI
Jul 7

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

arXiv:2601. 22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data.

By Baorui Ma, Jiahui Yang, Donglin Di, Xuancheng Zhang, Jianxun Cui, Hao Li, Yan Xie, Wei Chen
arXiv Computer Vision
Oct 12

DeFM: Learning Foundation Representations from Depth for Robotics

DeFM is a self‑supervised foundation model trained solely on depth images for robotics. It uses a DINO‑style self‑distillation objective on 60 M curated depth images to learn geometric and semantic representations that generalize across environments, tasks, and sensors. The model incorporates a novel input normalization strategy for metric awareness, can be distilled into compact versions for resource‑constrained systems, and achieves state‑of‑the‑art performance on depth‑based classification, segmentation, navigation, locomotion, and manipulation benchmarks, with strong sim‑to‑real transfer.

By Manthan Patel, Jonas Frey, Mayank Mittal, Fan Yang, Alexander Hansson, Amir Bar, Cesar Cadena, Marco Hutter
arXiv Computer Vision
Sep 4

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

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.

By Denis M. Akola, David F. Fouhey
arXiv Computer Vision
Sep 25

FounRef: Robust, Structure-Preserving, and Fast Metric Refinement of Frozen Monocular Foundation Priors with Sparse Anchors

FounRef is a training‑free method that refines frozen monocular foundation priors into dense metric depth by aligning them with sparse metric anchors. It validates anchors against the prior’s predictions, rejects misaligned ones, and applies a structure‑preserving solver to correct depth globally and locally while preserving fine geometry. The approach works out of the box on unseen cameras and scenes, achieving up to 24% lower depth error, 92% lower surface‑normal noise, and nearly 15× faster inference than a leading depth‑completion network.

By Dan Halperin, Mirko M\"ahlisch