datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
3dgsmatterport3d_region_mcmc_3dgsLicense Notice:This dataset is derived from Matterport3D.It follows the Matterport End User License Agreement for Academic Use of Model Data.See Matterport3D License for details.
3d-front-code
3D-Front-Code
RoomScript v4 Blender object programs, room-layout renders, code-only wall
architecture, and asset Blender artifacts derived from 3D-FRONT scene evidence.
Contents
13,917 object assets (reference and v4/best) in data/assets/*.tar
21,202 rooms (v4/best and code-only v4_wall/best) in data/rooms/*.tar
searchable JSONL indexes under metadata/
Each tar contains multiple samples while preserving the original
data/front_object_code/by_asset/... or… See the full description on the dataset page: https://huggingface.co/datasets/KevinFan111/3d-front-code.latent-3d-cache3dgs-output3DGS_Resultsmental_rotation_3d_procedural_v2bigym-all-tasks-3dgs-one-success
BiGym 全任务 3DGS 厨房壳 单成功轨迹集
发布状态：40/40 技术验证通过，视觉抽检通过；仓库保持，等待上游数据/壳资产再分发条款复核。
预览视频：previews/collection-preview.mp4
核心结果
官方 BiGym 任务：40/40
唯一任务：40
保存的 reward=1 episode：40
丢弃的 reward=0 候选：61（未进入成功 Parquet）
Transition / 每相机帧数：14,806 / 14,806
三相机 H.264 文件：120
相机：head 848×480、left wrist 640×480、right wrist 640×480，20 FPS
数据布局
reach/：3 个任务
long_horizon/：3 个任务
dishwasher/：10 个任务
tabletop/：24 个任务
task-manifest.csv：任务、demo UUID、seed、帧数、reward、动作哈希… See the full description on the dataset page: https://huggingface.co/datasets/eustance/bigym-all-tasks-3dgs-one-success.3dgsHi3DBench
Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validity
Yuhan Zhang*
·
Long Zhuo*
·
Ziyang Chu*
·
Tong Wu†
·
Zhibing Li
·
Liang Pan†
·
Dahua Lin
·
Ziwei Liu†
*Equal contribution †Corresponding authors
[Project page]
[ArXiv]
[Leaderboard]
[Github]
This is an annotation dataset for 3D quality evaluation… See the full description on the dataset page: https://huggingface.co/datasets/3DTopia/Hi3DBench.mental_rotation_3d_procedural3da-libero-training-assets
GAM LIBERO Training Assets
This dataset repository contains the assets needed to fine-tune GAM on LIBERO.
Layout
checkpoints/track4world_da3.pth
data/libero_noop/<suite>/*.hdf5
data/libero_noop/_stats/*.json
configs/training/libero_unified/
track4world_da3.pth is the DA3-Giant base checkpoint. The LIBERO HDF5 files contain embedded RGB, proprioception, actions, and depth used by the public GAM training configs.
Code:… See the full description on the dataset page: https://huggingface.co/datasets/SeonghuJeon/3da-libero-training-assets.3d-dlp-repro-genericshapes-rgb
GenericShapes-RGB — synthetic RGB-voxel tabletop scenes
Training/evaluation corpus built for an independent reproduction of ICML 2026 paper #10351,
3D-DLP: Self-supervised 3D Object-centric Scene Representation Learning
(OpenReview vIotI25gJz, code
github.com/Eubooks3003/3d-dlp).
The paper's GenericShapes corpus (Appendix B.2) is described but not released, and the authors'
released generator scripts/generate_ply.py
writes colourless point clouds — the "RGB-coloured variant used… See the full description on the dataset page: https://huggingface.co/datasets/rvt832/3d-dlp-repro-genericshapes-rgb.3d-pinn-dim55-processed_datasettoricblm-dataset-state-toricblm-structure-priority-balanced-3day-20260709t185034z-epoch-001
ToricBLM dataset state: toricblm-structure-priority-balanced-3day-20260709T185034Z epoch 001
This dataset repo records the exact local training-data state visible to the dynamic epoch launcher.
It intentionally stores manifests and audit records rather than duplicating large Parquet shards.
Special checkpoint: toricblm-structure-priority-balanced-3day-20260709T185034Z_epoch_001_special_structure_current_step_002000.pt
Checkpoint repo: AmelieSchreiber/ToricGT_160M_FoT
Curriculum… See the full description on the dataset page: https://huggingface.co/datasets/AmelieSchreiber/toricblm-dataset-state-toricblm-structure-priority-balanced-3day-20260709t185034z-epoch-001.toricblm-dataset-state-toricblm-structure-priority-balanced-3day-20260709t185034z-epoch-002
ToricBLM dataset state: toricblm-structure-priority-balanced-3day-20260709T185034Z epoch 002
This dataset repo records the exact local training-data state visible to the dynamic epoch launcher.
It intentionally stores manifests and audit records rather than duplicating large Parquet shards.
Special checkpoint: toricblm-structure-priority-balanced-3day-20260709T185034Z_epoch_002_special_structure_delta_step_002750.pt
Checkpoint repo: AmelieSchreiber/ToricGT_160M_FoT
Curriculum… See the full description on the dataset page: https://huggingface.co/datasets/AmelieSchreiber/toricblm-dataset-state-toricblm-structure-priority-balanced-3day-20260709t185034z-epoch-002.3d-syntree-multidataset3D_STU_test_seed2025
3D STU test — point OOD scores (seed 2025)
Bundle: 3D_STU_test_seed2025.tar.gz
Method: NDP+EE reproduce (Mask4Former3D, seed 2025, last-epoch.ckpt)
Val FPR@95 (development): 0.39% on STU val
Frames: 14608 / 51 sequences; scores fmt=%.6f in tar
See 3D_STU_test_seed2025.manifest.json for SHA256 and provenance.
3-digit-arithmetic-scratchpad-traces
Contents
Split
Rows
train
100,000
validation
4,000
test
4,000
total
108,000
Splits are prompt-disjoint — no expression appears in more than one split,
and commutative swaps and trace keys are de-duplicated across splits to prevent
split leakage.
Operation
Rows
×
32,000
÷
32,000
+
22,000
−
22,000
Operands lie in [−999, 999]. Division answers use a fixed DDD.ddd form
(round-half-up to three decimals); division by zero is an atomic <nan>.… See the full description on the dataset page: https://huggingface.co/datasets/vmal/3-digit-arithmetic-scratchpad-traces.VLM-3DPOSE-DATASCOPE-3D-claim-graphs
SCOPE-3D Expert-Reviewed MVSA Claim Graphs
This repository hosts the final manually corrected MVSA claim graphs used by
SCOPE-3D to build the MVScanQA-SCOPE training and validation splits.
Files
claim_graphs/mvscanqa_train_claims.final_clean.jsonl: 7,875 train questions.
claim_graphs/mvscanqa_val_claims.final_clean.jsonl: 2,229 validation questions.
claim_graphs/SHA256SUMS: checksums used by the project download script.
Each JSONL record contains the original… See the full description on the dataset page: https://huggingface.co/datasets/SanGibb02/SCOPE-3D-claim-graphs.scannetpp_v2_default_fixed_xyz_3dgsPlease note for this version of ScanNet++ v2 3DGS scenes, we disabled the optimization of 3DGS centers and used exactly the same number and locations as the initializing point clouds, so we can directly copy the original scene sem.seg. labels to the GS. This did cause strong limitation on the resulted 3DGS quality and this part of the data are not used for SceneSplat vision-language pretraining, but only for self-supervised training and later SceneSplat main paper Table 4 experiments of… See the full description on the dataset page: https://huggingface.co/datasets/GaussianWorld/scannetpp_v2_default_fixed_xyz_3dgs.