Stereo and Triangulation
Rectification, disparity, the Z = fB/d relation, linear triangulation, and depth uncertainty.
Key Terms
Python Tools
Textbook Reference
Topics Covered
- 1Stereo rectification to horizontal epipolar lines
- 2Disparity from block matching and cost aggregation
- 3The depth-disparity relation Z = fB/d and its sensitivity
- 4Linear (DLT) triangulation and the midpoint method
- 5Propagating matching error to depth uncertainty
Learning Outcomes
- Explain rectification and its effect on the correspondence search
- Convert disparity to metric depth and analyze its error
- Triangulate 3D points by DLT and assess their uncertainty
3D reconstruction walkthrough
Depth Estimation & Introductory Structure from Motion
Three connected ideas, one step at a time: how two cameras turn a pixel shift into a distance, how two views triangulate a single point, and how many views recover structure and motion together.
Step 1 of 8 · Two cameras
Two cameras, one rigid offset
A rectified stereo rig is two identical cameras side by side, looking the same way. The distance between their centres is the baseline B — the only thing that makes depth recoverable at all.
Look for
In the 3D view, the amber bar between Cᴸ and Cᴿ is the baseline.
xL
452.0 px
xR
360.1 px
disparity d
91.92 px
estimated Z
4.525 m
true Z
4.500 m
depth error
0.025 m
uncertainty ±
0.039 m
baseline B
0.80 m
Try it
Lecture PDFs
Stereo and Triangulation
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