论文标题
大规模光度束调节
Large Scale Photometric Bundle Adjustment
论文作者
论文摘要
直接方法显示了对视觉探光和猛击的希望，从而使基于特征方法的方法具有更高的准确性和鲁棒性。但是，互联网图像的离线3-D重建尚未受益于密集几何和相机参数的关节光度优化。诸如缺乏亮度恒定的问题以及大量的数据，使这一任务更具挑战性。这项工作提出了一个框架，该框架使用光度计算成本与本地照明变化不变，以共同优化数百万个场景点和数百个相机姿势和内在的框架。大规模的坦克和寺庙基准表明，它对基于特征的捆绑套件调整的度量重建精度的提高。我们进一步展示了互联网照片集的定性重建改进，并具有挑战性的照明和相机内在的多样性。
Direct methods have shown promise on visual odometry and SLAM, leading to greater accuracy and robustness over feature-based methods. However, offline 3-d reconstruction from internet images has not yet benefited from a joint, photometric optimization over dense geometry and camera parameters. Issues such as the lack of brightness constancy, and the sheer volume of data, make this a more challenging task. This work presents a framework for jointly optimizing millions of scene points and hundreds of camera poses and intrinsics, using a photometric cost that is invariant to local lighting changes. The improvement in metric reconstruction accuracy that it confers over feature-based bundle adjustment is demonstrated on the large-scale Tanks & Temples benchmark. We further demonstrate qualitative reconstruction improvements on an internet photo collection, with challenging diversity in lighting and camera intrinsics.
