RI: Small: Learning to Eliminate Heuristics in Stereo Vision

RI：小：学习消除立体视觉中的启发式方法

• 批准号: 1527294
• 负责人: Philippos Mordohai
• 金额: $ 43.2万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527294&HistoricalAwards=false
• 关键词: RI; Small; Learning; Eliminate; Heuristics

This project develops technologies to improve stereo and multi-view stereo algorithms by removing heuristics and hand-tuning using machine learning techniques. Stereo matching is the process of estimating depth of points, or 3D coordinates in a scene, and is enabled by the estimation of correspondences between pixels or other primitives in two or more images. Even the most successful current stereo matching algorithms, however, use a large number of heuristics. The developed methods from this project eliminate the heuristics from binocular and multi-view stereo matching and deliver algorithms with higher accuracy, interpretability of the results and higher portability to different settings. Stereo vision plays an important role in many applications, such as 3D modeling, augmented reality, driver assistance, autonomous navigation and human computer interaction. The educational and outreach aspects of the project focus on involving K-12 and undergraduate students in STEM education and research. This research addresses stereo vision by training classifiers that learn from pairs, or larger sets of images, with ground truth depth to make more accurate predictions about unobserved data than those obtained by hand-crafted rules. The approach is comprehensive and tackles all stages of the binocular stereo matching process, including the matching cost function, cost aggregation, optimization and refinement. Representations for multi-view stereo based on surface patches, depth maps or occupancy grids and the corresponding algorithms are also supported by the same framework. Random forest classifiers are well suited for use in inhomogeneous feature spaces and classifier calibration can ensure that their outputs are close to the true posterior probabilities of the classes under consideration. The resulting algorithms and findings can be transferred to other computer vision problems that require pixel correspondences, such as optical flow estimation, image stitching and template matching.

该项目开发技术，以改善立体和多视图立体算法，通过消除障碍和手动调整使用机器学习技术。立体匹配是估计点的深度或场景中的3D坐标的过程，并且通过估计两个或更多个图像中的像素或其他基元之间的对应关系来实现。然而，即使是当前最成功的立体匹配算法，也使用大量的几何学。该项目开发的方法消除了双目和多视图立体匹配的复杂性，并提供了具有更高精度、结果可解释性和更高可移植性的算法。立体视觉在三维建模、增强现实、驾驶辅助、自主导航和人机交互等领域有着重要的应用。该项目的教育和推广方面的重点是让K-12和本科生参与STEM教育和研究。这项研究通过训练分类器来解决立体视觉问题，这些分类器从成对或更大的图像集合中学习，具有地面真实深度，从而对未观察到的数据进行比手工规则更准确的预测。该方法是全面的，解决了双目立体匹配过程的所有阶段，包括匹配成本函数，成本聚合，优化和细化。基于曲面片、深度图或占用网格的多视图立体表示以及相应的算法也由相同的框架支持。随机森林分类器非常适合在非均匀特征空间中使用，并且分类器校准可以确保其输出接近所考虑的类的真实后验概率。由此产生的算法和发现可以转移到其他需要像素对应的计算机视觉问题，如光流估计，图像拼接和模板匹配。