城市级大场景中快速鲁棒的相机定位方法研究

Camera localization refers to obtaining the 6-DOF camera pose relative to a 3D model, which is typically estimated from the correspondences between 2D points in the image and 3D points in the model.As the fast development of autonomous vehicles, intelligent robots and augmented reality technologies, the investigation of camera localization in city-scale outdoor scenes becomes more and more important. However, due to the enormous 3D model, viewpoint and day-night changes, weather variations etc., it is very difficult to obtain the 2D-3D correspondences both fast and accurately, and the existing methods are far from the requirements of practical usages on the robustness, the speed and accuracy. This project will focus on large scale 2D-3D matching which is the essential problem in camera localization,and aims to substantially improve its accuracy and efficiency through the development of better local features and indexing methods.The main contents are as follows: (1) improving the discriminability of local features by using convolutional neural networks to integrate the spatial context information from the entire image. (2) extracting viewpoint and illumination invariant local features by exploiting the fact that Generative Adversarial Networks can reconstruct images under some controlled factors. (3) using random decision forests to indexing the 3D points, and training the forests with supervisions in the database to improve the efficiency of 2D-3D matching.

相机定位指根据拍摄的图像获取相机相对于场景三维模型的6自由度姿态，通常可由图像二维点和模型三维点的对应关系来估计。随着自动驾驶、智能机器人、增强现实等技术的蓬勃发展，研究城市级室外大场景中的相机定位凸显出越来越重要的实际意义。然而由于模型庞大、视角差异、白-夜和天气变化等因素，快速准确地建立二维-三维对应关系难度很大，现有的定位方法在鲁棒性、速度和精度上都远未达到实际应用的要求。本项目将针对大规模二维-三维匹配这一核心问题进行研究，旨在以局部特征提取和三维点索引为突破口，实质性地提高匹配的准确性和速度，进而提升定位的鲁棒性、速度和精度。具体内容包括：(1)用卷积神经网络融合整体图像的空间上下文信息来提升局部特征的区分度(2)利用生成对抗网络可在受控条件下重构图像的特性提取具有视角和光照不变性的局部特征(3)采用随机决策森林对三维点进行有监督的索引，充分利用数据库中的监督信息提高匹配的效率。