arXiv Machine Learning
Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation
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DepthART: Scaling Foundation Monocular Depth to Tiny Models
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Monocular Depth Estimation from a Single Image: Progress and Opportunities
arXiv:2609.01172v1 Announce Type: new Abstract: Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruct...
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M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo
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Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention
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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.
arXiv AI
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PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation
PXDepth is a monocular depth estimation model that separates global context modeling from pixel-level depth prediction. It uses a large-patch Vision Transformer to capture scene context and a pixel-space predictor with Context‑Modulated Pixel Transformer blocks to preserve high‑resolution spatial details. The approach maintains fine structures and sharp boundaries while achieving competitive global depth accuracy in zero‑shot benchmarks.