开始时间: 12/21/2023 持续时间: 4-6 hours/week
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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
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.
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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本课程侧重于从不同视点拍摄的图像中恢复场景的 3D 结构。我们首先建立相机的综合几何模型，然后开发一种方法来查找（校准）相机模型的内部和外部参数。然后，我们展示了如何使用两个这样的校准相机（其相对位置和方向已知）来恢复场景的 3D 结构。这就是我们所说的简单双目立体。接下来，我们解决两个相机的相对位置和方向未知的未校准立体问题。有趣的是，仅从相机拍摄的两张图像中，我们都可以确定相机的相对位置和方向，然后使用这些信息来估计场景的 3D 结构。接下来，我们关注动态场景的问题。给定包含移动对象的场景的两个图像，我们将展示如何计算图像中每个点的运动。图像中点的这种明显运动称为光流。光流估计允许我们跟踪视频序列上的场景点。接下来，我们考虑使用移动相机拍摄的场景视频，其中相机的运动是未知的。我们从运动中呈现结构，将此类视频中的跟踪特征作为输入，不仅确定场景的 3D 结构，还确定相机相对于场景的移动方式。我们在课程中开发的方法广泛用于对象建模、3D 站点建模、机器人技术、自主导航、虚拟现实和增强现实。
