[2509.04338] From Editor to Dense Geometry Estimator

Computer Science > Computer Vision and Pattern Recognition arXiv:2509.04338 (cs) [Submitted on 4 Sep 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:From Editor to Dense Geometry Estimator Authors:JiYuan Wang, Chunyu Lin, Lei Sun, Rongying Liu, Lang Nie, Mingxing Li, Kang Liao, Xiangxiang Chu View a PDF of the paper titled From Editor to Dense Geometry Estimator, by JiYuan Wang and 7 other authors View PDF HTML (experimental) Abstract:Leveraging visual priors from pre-trained text-to-image (T2I) generative models has shown success in dense prediction. However, dense prediction is inherently an image-to-image task, suggesting that image editing models, rather than T2I generative models, may be a more suitable foundation for fine-tuning. Motivated by this, we conduct a systematic analysis of the fine-tuning behaviors of both editors and generators for dense geometry estimation. Our findings show that editing models possess inherent structural priors, which enable them to converge more stably by ``refining" their innate features, and ultimately achieve higher performance than their generative counterparts. Based on these findings, we introduce \textbf{FE2E}, a framework that pioneeringly adapts an advanced editing model based on Diffusion Transformer (DiT) architecture for dense geometry prediction. Specifically, to tailor the editor for this deterministic task, we reformulate the editor's original flow matching loss into the ``consistent velocity" training obje...