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---single-viewpoint
课程评论：没有评论
This course focuses on the recovery of the 3D structure of a scene from its 2D images. In particular, we are interested in the 3D reconstruction of a rigid scene from images taken by a stationary camera (same viewpoint). This problem is interesting as we want the multiple images of the scene to capture complementary information despite the fact that the scene is rigid and the camera is fixed. To this end, we explore several ways of capturing images where each image provides additional information about the scene. In order to estimate scene properties (depth, surface orientation, material properties, etc.) we first define several important radiometric concepts, such as, light source intensity, surface illumination, surface brightness, image brightness and surface reflectance. Then, we tackle the challenging problem of shape from shading - recovering the shape of a surface from its shading in a single image. Next, we show that if multiple images of a scene of known reflectance are taken while changing the illumination direction, the surface normal at each scene point can be computed. This method, called photometric stereo, provides a dense surface normal map that can be integrated to obtain surface shape. Next, we discuss depth from defocus, which uses the limited depth of field of the camera to estimate scene structure. From a small number of images taken by changing the focus setting of the lens, a dense depth of the scene is recovered. Finally, we present a suite of techniques that use active illumination (the projection of light patterns onto the scene) to get precise 3D reconstructions of the scene. These active illumination methods are the workhorse of factory automation. They are used on manufacturing lines to assemble products and inspect their visual quality. They are also extensively used in other domains such as driverless cars, robotics, surveillance, medical imaging and special effects in movies.
Name:Getting Started: 3D Reconstruction - Single Viewpoint
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Name:Radiometry and Reflectance
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Name:Photometric Stereo
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Name:Shape from Shading
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Name:Depth from Defocus
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Name:Active Illumination Methods
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本课程侧重于从 2D 图像中恢复场景的 3D 结构。特别是，我们对从静止相机（相同视点）拍摄的图像中对刚性场景进行 3D 重建感兴趣。这个问题很有趣，因为我们希望场景的多个图像捕获互补信息，尽管场景是刚性的并且相机是固定的。为此，我们探索了几种捕获图像的方法，其中每张图像都提供有关场景的附加信息。为了估计场景属性（深度、表面方向、材料属性等），我们首先定义了几个重要的辐射测量概念，例如光源强度、表面照明、表面亮度、图像亮度和表面反射率。然后，我们从阴影中解决形状的挑战性问题——从单个图像中的阴影中恢复表面的形状。接下来，我们展示了如果在改变照明方向的同时拍摄多个已知反射率场景的图像，则可以计算每个场景点的表面法线。这种称为光度立体的方法提供了一个密集的表面法线贴图，可以将其集成以获得表面形状。接下来，我们讨论散焦的深度，它使用相机的有限景深来估计场景结构。从通过改变镜头的焦距设置拍摄的少量图像中，恢复了场景的密集深度。最后，我们提出了一套使用主动照明（将光图案投影到场景上）来获得场景的精确 3D 重建的技术。这些主动照明方法是工厂自动化的主力军。它们在生产线上用于组装产品并检查其视觉质量。它们还广泛用于其他领域，例如无人驾驶汽车、机器人、监控、医学成像和电影特效。
