arXiv AI August 19, 2026 By Zhiyuan Yuan, Guanying Chen, Lingteng Qiu, Ruimao Zhang, Shuguang Cui, Xiaochun Cao

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.

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 AI.

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

computer-vision efficiency benchmarks safety

arXiv AI

Jul 15

ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning

arXiv:2607. 12433v1 Announce Type: cross Abstract: Diffusion models have recently become the dominant paradigm for monocular depth estimation (MDE).

By Zijie Wang, Wei Zhang, Weiming Zhang, Xiao Tan, Weikai Chen, Xiaoxu Li, Guanbin Li

diffusion computer-vision

arXiv Computer Vision

Aug 24

M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo

arXiv:2608.20788v1 Announce Type: new Abstract: Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or...

By Byeonggwon Lee, Sanggi Lee, Siwoo Lee, Khang Truong Giang, Soohwan Song

benchmarks

Hugging Face Trending Papers

Jul 2

FoundDP: Revisiting Weak Disparity Observability in Dual-Pixel Depth Estimation

Dual-pixel (DP) imaging enables metric depth estimation from a single camera using sub-aperture disparity. However, the extremely small effective baseline limits disparity observability, leading to structural degradation and depth failure in textureless, low-contrast, or downsampled regions.

llms computer-vision benchmarks safety

Hugging Face Trending Papers

Jun 11

Modality Forcing for Scalable Spatial Generation

Text-to-image (T2I) models contain rich spatial priors. Synthesizing photorealistic, cluttered scenes requires an understanding of geometry, including perspective and relative scale.

diffusion benchmarks

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

llms computer-vision multimodal benchmarks