The most unreliable direction of computer vision in 2023: 3D reconstruction learning route combing! How to get started at a glance

In recent years, there have been many hot topics in the field of artificial intelligence, some of which last for a long time, and some of which are just like a flash in the pan. With the rapid development of technologies such as robots, autonomous driving, and AR, a large number of unicorn companies have emerged in these fields. A large number of products with multiple application scenarios such as 3D perception, fusion positioning, reconstruction, and ranging have been developed. The term 3D reconstruction has become popular again.

In simple terms, 3D reconstruction (3D Reconstruction) refers to the use of cameras and other sensors to capture real-world objects and scenes, and process them through computer vision technology to obtain a 3D model of the object.

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3D reconstruction is one of the core technologies in fields such as augmented reality (AR), mixed reality (MR), robot navigation, and autonomous driving. Similarly, many large factories will require mastering 3D reconstruction theory and technology when recruiting related positions.

As an interdisciplinary subject, 3D reconstruction involves advanced mathematics, computer vision, computer graphics and other disciplines. The main technologies involved are: multi-view stereo geometry, depth map estimation, point cloud processing, mesh reconstruction and optimization, texture mapping, Marr Cove random field, graph cut, etc.

Many students go to study the algorithm framework by themselves because the instructor assigned this direction. However, there is no way to master it without guidance, which leads to great pain. The 3D algorithm has a lot of content and a steep learning curve.

We invite Mr. Hally, who has profound experience in visual measurement and 3D reconstruction, to explain 3D reconstruction, imaging principles and camera calibration. Teacher Hally has many years of solid accumulation in the industry and academia, and can guide students from the introduction of traditional vision to the practical transformation in the field of deep learning and familiarity with various visual software and computing libraries, such as Open3d, PCL, opencv, SfM, etc. .

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Collection of latest papers on 3D reconstruction

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Traditional reconstruction methods use photometric consistency to compute dense 3D information. Although these methods are in ideal Lambertian scenarios, the accuracy is already very high. But there are also some common limitations, such as weak textures, highly reflective and repeated textures, etc., which make reconstruction difficult or incomplete. Therefore, traditional 3D reconstruction methods still have a lot of room for improvement in terms of reconstruction integrity.

On the other hand, camera calibration is used as a method to calculate the relative mapping relationship between the camera's two-dimensional image and the three-dimensional world. It is no exaggeration to say that camera calibration is the basis of all 3D vision. It is widely used in binocular vision, hand-eye robotic arm, robot positioning and mapping, image stitching, 3D reconstruction and other scenarios.

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Origin blog.csdn.net/CV_Autobot/article/details/131318732