Research map / Point clouds / Registration and matching
Quantum and certifiable correspondence
Solve point-set correspondence and transformation estimation with quantum or certifiably robust solvers.
8 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2017–2025) · an approach in Registration and matching, keyword cluster “rigid · point registration · non”
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Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
0 papers in 2023–24 and 2 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 0.0% to 4.3%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2017 · Discriminative Optimization: Theory and Applications to Point Cloud Registration (CVPR 2017 · 37 citations)
- 2020 · A Quantum Computational Approach to Correspondence Problems on Point Sets (CVPR 2020 · 28 citations)
- 2021 · Self-Supervised Geometric Perception (CVPR 2021 · 30 citations)
- 2022 · ARCS: Accurate Rotation and Correspondence Search (CVPR 2022 · 15 citations)
- 2025 · QuCOOP: A Versatile Framework for Solving Composite and Binary-Parametrised Problems on Quantum Annealers (CVPR 2025 · 3 citations)
Earliest papers here
- Discriminative Optimization: Theory and Applications to Point Cloud Registration (CVPR 2017 · 37 citations)
- A Quantum Computational Approach to Correspondence Problems on Point Sets (CVPR 2020 · 28 citations)
- One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers (NeurIPS 2020 · 4 citations)
What has been tried
8 papers here report 54 measured changes (a component added, removed or swapped, compared with a baseline): helped 61%, no clear effect 13%, hurt 22%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Use it
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Related approaches
Same keyword cluster
- Non-rigid point set registration (42 papers)
Elsewhere in the topic
- Learned feature-based registration (66 papers)
- Robust inlier selection for registration (29 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).