让3D几何预测模型高效处理2K分辨率图像，无需重训练。
2K Retrofit: Entropy-Guided Efficient Sparse Refinement for High-Resolution 3D Geometry Prediction
- 用粗略预测+熵值筛选高不确定性区域进行精修
- 在2K图像上实现高精度输出且计算开销极小
- 适合自动驾驶、机器人等需要高清3D感知的场景
高分辨率几何预测对自动驾驶、机器人和增强/混合现实中的鲁棒感知至关重要，但现有基础模型在真实高分辨率场景下存在可扩展性瓶颈。直接对2K图像进行推理会带来巨大的计算与内存开销，难以实际部署。为此，我们提出2K Retrofit，一种无需修改或重新训练主干网络即可实现任意几何基础模型高效2K分辨率推理的新框架。该方法利用快速粗略预测，并基于熵值选择高不确定性区域进行稀疏精修，以极小开销获得高保真度的2K输出。在多个主流基准上的大量实验表明，2K Retrofit在准确率和速度上均达到当前最优水平，有效弥合了高分辨率3D视觉研究进展与可扩展部署之间的差距。代码将在论文接受后发布。
原文摘要 · Abstract (English)
High-resolution geometric prediction is essential for robust perception in autonomous driving, robotics, and AR/MR, but current foundation models are fundamentally limited by their scalability to real-world, high-resolution scenarios. Direct inference on 2K images with these models incurs prohibitive computational and memory demands, making practical deployment challenging. To tackle the issue, we present 2K Retrofit, a novel framework that enables efficient 2K-resolution inference for any geometric foundation model, without modifying or retraining the backbone. Our approach leverages fast coarse predictions and an entropy-based sparse refinement to selectively enhance high-uncertainty regions, achieving precise and high-fidelity 2K outputs with minimal overhead. Extensive experiments on widely used benchmark demonstrate that 2K Retrofit consistently achieves state-of-the-art accuracy and speed, bridging the gap between research advances and scalable deployment in high-resolution 3D vision applications. Code will be released upon acceptance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。