RI: Small: RUI: Image Matching in the Wild

该项目旨在推进立体视觉和光流算法在具有挑战性的现实世界条件下工作。 它贡献了新的算法方法，新的高分辨率数据集与地面真理，并更新了米德尔伯里基准。算法的进步包括快速匹配算法，采用辐射和几何自校准，包括智能数据项，建立噪声和颜色模型在匹配过程中，和新的基于层的表面重建算法与显式推理的半遮挡区域，反射，和透明度。 新的数据集反映了当前的挑战，包括具有复杂遮挡的真实世界场景的高分辨率图像，镜面反射表面和不同照明下的反射，并使用不同的相机拍摄。 本科生积极参与这项研究的所有组成部分。该项目在沿着几个方面具有强大的潜在影响。 新的数据集和基准挑战了社区，并成为新研究的催化剂。 算法的进步允许利用在线图像的爆炸，并实现现实世界的应用，如自动驾驶，地理定位和整个城市的自动3D重建。 最后，该项目使佛蒙特州农村一所文科学院的本科生接触到研究、实验和发现的世界。

This project aims to advance stereo vision and optical flow algorithms to work in challenging real-world conditions. It contributes novel algorithmic approaches, new high-resolution datasets with ground truth, and an update to the Middlebury benchmarks.The algorithmic advances include fast matching algorithms that employ radiometric and geometric self-calibration, including smart data terms that establish noise and color models during the matching process, and novel layer-based surface reconstruction algorithms with explicit reasoning about half-occluded regions, reflections, and transparency. The new datasets reflect current challenges, including high-resolution images of real-world scenes with complex occlusions, specular surfaces and reflections under different illuminations and taken with different cameras. Undergraduate students are actively involved in all components of this research.The project has strong potential impact along several fronts. The new datasets and benchmarks challenge the community and serve as catalysts for new research. The algorithmic advances allow harnessing the explosion of images available online, and enable real-world applications such as automated driving, geolocation, and automatic 3D reconstruction of whole cities. Finally, the project exposes undergraduates at a liberal-arts college in rural Vermont to the world of research, experimentation, and discovery.