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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/3d-reconstruction-multiple-viewpoints
课程评论：没有评论
课程名称：3D重建 - 多视角 课程概述： 本课程聚焦于从不同视角拍摄的图像中恢复场景的三维结构。我们首先建立一个全面的相机几何模型，然后开发一种方法来寻找（标定）相机模型的内部和外部参数。接着，我们展示了如何利用两个已标定的相机（其相对位置和方向已知）来恢复场景的三维结构，这被称为简单的双目立体视觉。接下来，我们研究了未标定立体视觉的问题，在这种情况下，两个相机的相对位置和方向未知。有趣的是，仅仅通过相机拍摄的两幅图像，我们就能确定相机的相对位置和方向，并利用这些信息估计场景的三维结构。 随后，我们关注动态场景的问题。当给定包含运动物体的场景的两幅图像时，我们展示了如何计算图像中每个点的运动。图像中点的表观运动称为光流。光流估计使我们能够在视频序列中跟踪场景点。接着，我们考虑在未知相机运动下拍摄的场景视频。我们介绍了从运动中恢复结构的方法，该方法以跟踪的特征为输入，确定不仅场景的三维结构，还包括相对于场景的相机运动。我们在课程中开发的方法广泛应用于物体建模、三维场地建模、机器人技术、自动导航、虚拟现实和增强现实。 课程大纲： 1. 开始：3D重建 - 多视角 2. 相机标定 3. 未标定立体视觉 4. 光流 5. 从运动中恢复结构
Name:Getting Started: 3D Reconstruction - Multiple Viewpoints
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Name:Camera Calibration
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Name:Uncalibrated Stereo
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Name:Optical Flow
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Name:Structure from Motion
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This course focuses on the recovery of the 3D structure of a scene from images taken from different viewpoints. We start by first building a comprehensive geometric model of a camera and then develop a method for finding (calibrating) the internal and external parameters of the camera model. Then, we show how two such calibrated cameras, whose relative positions and orientations are known, can be used to recover the 3D structure of the scene. This is what we refer to as simple binocular stereo. Next, we tackle the problem of uncalibrated stereo where the relative positions and orientations of the two cameras are unknown. Interestingly, just from the two images taken by the cameras, we can both determine the relative positions and orientations of the cameras and then use this information to estimate the 3D structure of the scene. Next, we focus on the problem of dynamic scenes. Given two images of a scene that includes moving objects, we show how the motion of each point in the image can be computed. This apparent motion of points in the image is called optical flow. Optical flow estimation allows us to track scene points over a video sequence. Next, we consider the video of a scene shot using a moving camera, where the motion of the camera is unknown. We present structure from motion that takes as input tracked features in such a video and determines not only the 3D structure of the scene but also how the camera moves with respect to the scene. The methods we develop in the course are widely used in object modeling, 3D site modeling, robotics, autonomous navigation, virtual reality and augmented reality.
