Registration and matching: research map

145 accepted papers on Registration and matching in Point clouds, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 4 approaches. The busiest year so far is 2025.

Within Point clouds, its share grew from 7.7% in 2023–24 to 9.0% in 2025–26 (35 → 46 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

cloud registration · overlap · feature · 95 papers

Approaches in this cluster:

Learned feature-based registration (66 papers)
Register point clouds with learned features, multi-view and image cues, and unsupervised training.
Robust inlier selection for registration (29 papers)
Reject outlier correspondences using geometric transformers, inlier evaluation and randomized solvers.

Most cited and most cited since 2024:

PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet (CVPR 2019 · 891 citations)
Predator: Registration of 3D Point Clouds With Low Overlap (CVPR 2021 · 715 citations)
FastMAC: Stochastic Spectral Sampling of Correspondence Graph (CVPR 2024 · 27 citations)
Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration (CVPR 2024 · 25 citations)

rigid · point registration · non · 50 papers