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Research map / 3D vision / Human pose estimation

Learned camera pose estimation

Estimates absolute and relative camera pose using learned features, pose regression and geometric solvers.

96 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Human pose estimation, keyword cluster “object pose · 6d · hand”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

25 papers in 2023–24 and 31 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 9.2% to 10.3%.

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

2016: 1162017: 1172018: 4182019: 7192020: 10202021: 10212022: 7222023: 10232024: 15242025: 12252026: 1926

Most cited paper of each year

Earliest papers here

What has been tried

74 papers here report 772 measured changes (a component added, removed or swapped, compared with a baseline): helped 78%, no clear effect 3%, hurt 16%, mixed 3%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
add the fully symmetric shared-weight architecture instead of asymmetric branchesScanNet1500 · AUC@532.71 -> 34.79HelpedReloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization (CVPR 2025 · 31 citations)
Use camera-pose estimates as geometry-learning conditioningBlendedMVS test set · Overall Chamfer distance0.0319 -> 0.0288HelpedFLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views (CVPR 2025 · 28 citations)
Remove exposure-adaptive ACTCambridge Shop Facade · median orientation error0.53° -> 1.20°HurtNeural Refinement for Absolute Pose Regression with Feature Synthesis (CVPR 2024 · 26 citations)
fuse Transformer pose with solver outputMatterport3D · mean rotation error (°)9.94 -> 4.93HelpedFAR: Flexible Accurate and Robust 6DoF Relative Camera Pose Estimation (CVPR 2024 · 17 citations)
add pose lossRealEstate10K Avg · PSNR14.646 -> 16.03HelpedUnifying Correspondence Pose and NeRF for Generalized Pose-Free Novel View Synthesis (CVPR 2024 · 15 citations)
Generate grid-wise features from pooled features with an FC layerCOCO Keypoint val · AP72.5 -> 71.1HurtSpatial-Aware Regression for Keypoint Localization (CVPR 2024 · 10 citations)
Use CosPlace-trained features instead of ImageNet featuresShopFacade and OldHospital · median pose error12 / 0.45 -> 12 / 0.55; 39 / 0.73 -> 46 / 0.80MixedThe Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement (CVPR 2024 · 10 citations)
Add rotation-only pre-training360VO-Seq4 · RRE0.1795 -> 0.0687HelpedPanoPose: Self-supervised Relative Pose Estimation for Panoramic Images (CVPR 2024 · 8 citations)
add depth inputCO3D, 8 images · camera center accuracy @ 0.187.8 -> 89.5HelpedMatrix3D: Large Photogrammetry Model All-in-One (CVPR 2025 · 6 citations)
Predict meta-points without support or refinementMP-100 1-shot · PCK@0.285.31 -> 79.31HurtMeta-Point Learning and Refining for Category-Agnostic Pose Estimation (CVPR 2024 · 5 citations)
add the global branch360SPR unseen · translation error (m)10.33±1.3 -> 3.85±0.3HelpedScene-agnostic Pose Regression for Visual Localization (CVPR 2025 · 4 citations)
increase evolution increments from none to fiveTurning-Round · SSIM0.936 -> 0.947HelpedDirect May Not Be the Best: An Incremental Evolution View of Pose Generation (AAAI 2024 · 4 citations)

What reviewers will expect

Datasets and baselines named in the 13 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • ARKitScenes · 4 of 13 papers
  • ScanNet++ · 3 of 13 papers
  • CO3D-v2 · 3 of 13 papers
  • RealEstate10K · 3 of 13 papers
  • ScanNet-1500 · 2 of 13 papers
  • 7-Scenes · 2 of 13 papers
  • MegaDepth · 2 of 13 papers
  • BlendedMVS · 2 of 13 papers

Baselines

  • DUSt3R · 3 of 13 papers
  • PoseDiffusion · 3 of 13 papers
  • MASt3R · 2 of 13 papers
  • COLMAP · 2 of 13 papers
  • VGGSfM · 2 of 13 papers
  • RelPose · 2 of 13 papers
  • RayDiffusion · 2 of 13 papers
  • Marepo · 2 of 13 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 56 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 40 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    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).

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