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 Computer Vision
5d ago

Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation

The paper introduces HypoDepth, an event-image monocular depth estimation framework that uses a discrete Depth Hypothesis Volume (DHV) to convert depth regression into a constrained search problem. By building a lightweight 3D cost volume between DHV features and contextual features, the method performs multi-scale correlation search for stable residual optimization, enabling efficient global-to-local refinement across resolutions. Experiments on DSEC and MVSEC show state‑of‑the‑art performance, strong zero‑shot generalization, and real‑time capability on resource‑limited devices.

By Daikun Liu, Teng Wang, Changyin Sun
arXiv Computer Vision
Sep 22

Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation

The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.

By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Pascal Fua
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