RI: Small: RUI: Image Matching in the Wild

RI：小：RUI：野外图像匹配

基本信息

批准号：
1320715
负责人：
Daniel Scharstein
金额：
$ 23.61万
依托单位：
Middlebury College
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-15 至 2017-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320715&HistoricalAwards=false
关键词：
RI Small RUI Image Matching

项目摘要

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.

该项目旨在推进立体视觉和光流算法，使其能够在充满挑战的现实条件下工作。它提供了新颖的算法方法、具有地面实况的新高分辨率数据集以及对 Middlebury 基准的更新。算法的进步包括采用辐射和几何自校准的快速匹配算法，包括在过程中建立噪声和颜色模型的智能数据术语。匹配过程，以及新颖的基于层的表面重建算法，具有关于半遮挡区域、反射和透明度的明确推理。新的数据集反映了当前的挑战，包括在不同照明下使用不同相机拍摄的具有复杂遮挡、镜面表面和反射的现实世界场景的高分辨率图像。本科生积极参与这项研究的所有组成部分。该项目在多个方面具有强大的潜在影响。新的数据集和基准对社区提出了挑战，并成为新研究的催化剂。算法的进步可以利用在线图像的爆炸式增长，并实现自动驾驶、地理定位和整个城市的自动 3D 重建等现实世界的应用。最后，该项目让佛蒙特州乡村一所文理学院的本科生接触到研究、实验和发现的世界。