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Recovering 3D structure and camera motion from images, including camera calibration, pose estimation, bundle adjustment, and simultaneous localization and mapping.
How does a robot know where it is? How can we build a 3D model from photos? This chapter tackles the fundamental problem of recovering 3D structure and camera motion from 2D images.
What is this chapter about? We learn to calibrate cameras, estimate camera poses, optimize 3D reconstructions, and build maps in real-time for autonomous navigation.
Why does this matter? These techniques enable:
How the topics connect: We start with camera calibration—measuring the intrinsic parameters of cameras. Pose estimation recovers where the camera is relative to known 3D points. Bundle adjustment jointly optimizes everything. SLAM does this in real-time as the robot explores.
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Recovering intrinsic parameters (focal length, principal point, distortion) from checkerboard images — the prerequisite for all 3D geometry.
Determining camera position and orientation from known 3D-2D correspondences via PnP — essential for AR and localization.
Global optimization and real-time mapping
Joint optimization of all cameras and 3D points by minimizing reprojection error — the gold standard for accurate reconstruction.
Simultaneous localization and mapping in real-time — building maps while tracking position for autonomous navigation.
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