提出新方法与数据集，精准估算野外极端视角图像的相对旋转
Extreme Rotation Estimation in the Wild
- 基于Transformer设计新算法，处理真实世界中视角极差的图像对
- 在自建数据集上显著优于传统旋转估计与3D重建方法
- 适合做视觉定位、三维重建的科研人员和工程师参考
我们提出一种用于估算互联网图像对之间相对3D朝向的技术，并构建了基准数据集，针对的是视角极度受限或无重叠区域的真实场景图像。以往研究多依赖受限3D环境，通过裁剪全景图模拟视角变化，但真实图像在外观和相机内参上差异巨大。本文提出基于Transformer的方法，在真实极端视角下实现高精度相对旋转估计，并构建了来自场景级互联网照片集合的ExtremeLandmarkPairs数据集。实验表明，该方法在多种极端视角的互联网图像对中表现优异，超越多个基线模型，包括专门的旋转估计方法和主流3D重建技术。
原文摘要 · Abstract (English)
We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate perspective images by cropping regions from panoramic views. However, real images captured in the wild are highly diverse, exhibiting variation in both appearance and camera intrinsics. In this work, we propose a Transformer-based method for estimating relative rotations in extreme real-world settings, and contribute the ExtremeLandmarkPairs dataset, assembled from scene-level Internet photo collections. Our evaluation demonstrates that our approach succeeds in estimating the relative rotations in a wide variety of extreme-view Internet image pairs, outperforming various baselines, including dedicated rotation estimation techniques and contemporary 3D reconstruction methods.
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