Computer Vision Interview 20 essential Q&A Updated 2026
ORB

ORB: 20 Essential Q&A

FAST + oriented BRIEF—fast real-time features on CPUs and mobile.

~10 min read 20 questions Intermediate
FASTBRIEFHammingreal-time
Quick Navigation
1. What is ORB?
2. FAST detector
3. BRIEF descriptor
4. Orientation from intensity
5. rBRIEF learning
6. Scale pyramid
7. Hamming match
8. ORB vs BRISK
9. ORB vs SIFT
10. 256 bits
11. Pixel pairs
12. Noise sensitivity
13. Steered BRIEF
14. Variance ordering
15. OpenCV ORB_create
16. NMS on FAST
17. Harris score on FAST
18. Embedded / SLAM
19. When ORB struggles
20. AKAZE note
1 What is ORB? ⚡ easy
Answer: Oriented FAST and Rotated BRIEF—free alternative to SIFT/SURF: FAST corners, orientation from intensity centroid, steered BRIEF binary descriptor with learned pattern (rBRIEF).
2 What is FAST? 📊 medium
Answer: Compare pixel to arc of circle pixels; corner if contiguous segment brighter/darker by threshold—very fast binary tests.
3 What is BRIEF? 📊 medium
Answer: Binary string from pairwise intensity comparisons in smoothed patch—256 bits typical; match with Hamming distance.
4 ORB orientation? 📊 medium
Answer: Intensity centroid vs corner—angle of vector from keypoint to centroid gives dominant direction to steer BRIEF.
5 What is rBRIEF? 🔥 hard
Answer: Learn subset of BRIEF pairs with low correlation under rotation—better variance and discrimination than random BRIEF when oriented.
6 Scale in ORB? ⚡ easy
Answer: Image pyramid with FAST+BRIEF at each level—approximates scale invariance like other multi-scale detectors.
7 Match ORB how? ⚡ easy
Answer: Hamming distance on bitstrings—very fast with POPCNT; BFMatcher or LSH variants.
8 ORB vs BRISK? 📊 medium
Answer: BRISK uses scale-space FAST-like sampling with learned pattern; both binary; tradeoffs in pattern and scale sampling differ.
9 ORB vs SIFT? 📊 medium
Answer: ORB: faster, compact binary, less discriminative on hard wide-baseline; SIFT: float 128-D, heavier, often stronger on difficult pairs.
10 Typical ORB length? ⚡ easy
Answer: 256 bits (32 bytes)—fixed in OpenCV default; tunable via WTA_K and descriptor size params.
11 BRIEF pixel pairs? 📊 medium
Answer: Predefined or learned (x_i, y_i) locations in patch; compare I(x_i)<I(y_i) → bit—rotation steers coordinates.
12 Binary descriptor noise? ⚡ easy
Answer: Sensitive to bit flips from noise—Gaussian smoothing before sampling reduces; strong blur changes comparisons.
13 Steered BRIEF? 🔥 hard
Answer: Rotate sampling coordinates by orientation θ before comparisons—makes descriptor rotation invariant.
14 WTA in ORB OpenCV? 🔥 hard
Answer: Can use multi-point WTA to build richer binary tests—implementation detail in ORB options.
15 OpenCV? ⚡ easy
Answer: cv2.ORB_create(nfeatures=500) → detectAndCompute. 
orb = cv2.ORB_create(500)
kp, des = orb.detectAndCompute(gray, None)
16 NMS on FAST? ⚡ easy
Answer: Suppress nearby FAST responses—ORB applies score (Harris) and grid to distribute features.
17 Harris on FAST? 📊 medium
Answer: Use Harris measure on candidate FAST points to rank corner quality.
18 Why ORB on mobile? ⚡ easy
Answer: Low memory, integer/bit ops, real-time VO/SLAM on CPUs without GPU.
19 When ORB struggles? 📊 medium
Answer: Strong viewpoint change, repetitive textures, heavy motion blur—may need SIFT/AKAZE or learning methods.
20 What is AKAZE briefly? 📊 medium
Answer: Nonlinear scale space + binary descriptor—often stronger than ORB on some benchmarks, still efficient.

ORB Cheat Sheet

Detect
  • FAST + pyramid
  • Harris score
Describe
  • rBRIEF steered
  • 256 bits
Match
  • Hamming
  • Very fast

💡 Pro tip: ORB = FAST + orientation + steered binary BRIEF.

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