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3D Vision Introduction MCQ
From 2D images to 3D structure: disparity, triangulation, cameras as rays, and why calibration matters.
3D
Structure
Stereo
Two views
Depth
Z
Point cloud
XYZ
3D vision basics
Recovering geometry from images uses cues like stereo disparity, motion parallax, shading, and learning-based monocular depth. Classical stereo relies on calibrated cameras and epipolar rectification to search along scanlines.
Disparity ↔ depth
For parallel rectified cameras, Z ∝ 1/d where d is horizontal disparity—baseline and focal length set the scale.
Building blocks
Projection
3D points map to 2D via intrinsics K and extrinsics [R|t].
Epipolar
Corresponding point lies on a line (1D search after rectification).
Point clouds
Sets of 3D samples from LiDAR, stereo fusion, or depth maps + unprojection.
RGB-D
Registered color + depth enables volumetric and SLAM methods.
Stereo pair
Correspondence → disparity map → depth map → mesh / point cloud