Wenqing Cui — accepted AI-conference papers

2026

Any Resolution Any Geometry: From Multi-View To Multi-Patch

Wenqing Cui, Zhenyu Li, Mykola Lavreniuk, Jian Shi, Ramzi Idoughi, Xiangjun Tang +1

CVPR 2026

Joint estimation of surface normals and depth is essential for holistic 3D scene understanding, yet high-resolution prediction remains difficult due to the trade-off between preserving fine local detail and maintaining global consistency. To address this challenge, we propose the Ultra Resolution Ge

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2026

PatchRefiner V2: Fast and Lightweight Real-Domain High-Resolution Metric Depth Estimation

Zhenyu Li, Wenqing Cui, Shariq Farooq Bhat, Peter Wonka

ICLR 2026poster

While current high-resolution depth estimation methods achieve strong results, they often suffer from computational inefficiencies due to reliance on heavyweight models and multiple inference steps, increasing inference time. To address this, we introduce PatchRefiner V2 (PRV2), which replaces heavy…

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