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Research map / 3D vision / 3D generation

2D-to-3D pretrained representations

Transfers pretrained 2D models to 3D, medical and molecular representation learning.

94 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in 3D generation, keyword cluster “semantic · occupancy · driving”

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.

29 papers in 2023–24 and 42 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 6.4% to 4.8%.

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: 2182019: 5192020: 4202021: 3212022: 7222023: 12232024: 17242025: 23252026: 1926

Most cited paper of each year

Earliest papers here

What has been tried

70 papers here report 752 measured changes (a component added, removed or swapped, compared with a baseline): helped 72%, no clear effect 4%, hurt 22%, mixed 1%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Add SAM-derived 3D supervoxels for trainingZero-shot external datasets · zero-shot performance50%HelpedVISTA3D: A Unified Segmentation Foundation Model For 3D Medical Imaging (CVPR 2025 · 69 citations)
add volume state estimationR2R val unseen · SR72.71 -> 75.80HelpedVolumetric Environment Representation for Vision-Language Navigation (CVPR 2024 · 36 citations)
replace atom dropping with coordinate noise at 0.5 ÅTox21 · ROC-AUC80.0 -> 78.6HurtMol-AE: Auto-Encoder Based Molecular Representation Learning With 3D Cloze Test Objective (ICML 2024 · 10 citations)
Add static EEG features to dynamic EEG featuresObject classification (72 categories) · top-1 accuracy (%)4.75 -> 5.91HelpedNeuro-3D: Towards 3D Visual Decoding from EEG Signals (CVPR 2025 · 8 citations)
Use adaptive λ₃ scheduling instead of training with L_seg only3 test blocks · Mean rank4.07 -> 1.41HelpedLearning Multimodal Volumetric Features for Large-Scale Neuron Tracing (AAAI 2024 · 8 citations)
Add multi-view inputs as separate batch samplesLucidAction · Spearman’s rank correlation (ρ)0.701 -> 0.730HelpedLucidAction: A Hierarchical and Multi-model Dataset for Comprehensive Action Quality Assessment (NeurIPS 2024 · 7 citations)
add cross-attention and GAT blocksHER2 · PCC0.659 -> 0.693HelpedASIGN: An Anatomy-aware Spatial Imputation Graphic Network for 3D Spatial Transcriptomics (CVPR 2025 · 6 citations)
Use range-null decomposition in the fidelity updateIXI, Gaussian mask, ×4 acceleration · PSNRHelpedNull Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction (AAAI 2024 · 6 citations)
Add CAD to the 2D networkAC3/4, Waterz · VOI1.0317 -> 0.9259HelpedCross-Dimension Affinity Distillation for 3D EM Neuron Segmentation (CVPR 2024 · 6 citations)
Add contrastive-inspired semantic alignment (CSA)OCTA-6M · MAE0.0765 -> 0.0741HelpedMuTri: Multi-view Tri-alignment for OCT to OCTA 3D Image Translation (CVPR 2025 · 5 citations)
add PI to the pre-trained LFE representationCOIL-20 · ACCclu54.33 -> 75.67HelpedSeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning (CVPR 2025 · 5 citations)
add lesion promptsMFIDDR test · accuracy81.64 -> 84.01HelpedLike an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading (AAAI 2025 · 3 citations)

What reviewers will expect

Datasets and baselines named in the 29 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • TotalSegmentator · 2 of 29 papers
  • ScanNet · 2 of 29 papers

Baselines

  • MedSAM · 2 of 29 papers
  • SegVol · 2 of 29 papers
  • nnU-Net · 2 of 29 papers
  • SAM3D · 2 of 29 papers
  • SuPreM · 2 of 29 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 71 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 37 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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