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arXiv Computer Vision By Liangjing Shao, Wanhao Liu, Jinsong Lin, Zhiwei Fang, Hongliang Ren

FlowMoDE: Coarse-to-fine Flow Matching for Detail-aware Sim-to-Real Monocular Depth Estimation

Read the original on arXiv Computer Vision →

FlowMoDE introduces a coarse‑to‑fine flow‑matching framework for monocular depth estimation that balances efficiency and detail reconstruction. The method first decodes coarse depth conditioned on diffused features from a pre‑trained semantic encoder, then refines fine structural patterns with expanded decoding layers. Trained only on a synthetic indoor dataset, FlowMoDE achieves a 14.1% lower relative error and a 9.9% lower Chamfer distance compared to state‑of‑the‑art methods across five real‑world indoor and outdoor datasets, and demonstrates strong generalization to realistic robotic scenes.

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 Computer Vision.

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