datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
VLADBenchRoboTwin2.0-DepthBEDLAM-depth
Dataset Mirror of BEDLAM Dataset (Depth Data Subset)
Project site: https://bedlam.is.tuebingen.mpg.de/
Please register at project site for additional information and data (Download section)
Related Hugging Face dataset mirror: BEDLAM
Dataset Information
Depth maps (EXR, 32-bit, 3.8TB)
Camera ground truth information is not included but can be found in the BEDLAM dataset mirror
Image/video data with motion blur is not included but can be found in the BEDLAM dataset… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Systems/BEDLAM-depth.UAVReason_depth
🗺️ UAVReason Depth
Depth maps and depth statistics for UAV-native multimodal reasoning and generation
📌 News
Paper: Can Vision-Language Models Think from the Sky? Unifying UAV Reasoning and Generation
arXiv: arXiv:2604.05377
VQA / caption / generation annotations: jarvissun/UAVReason_vqa
This dataset is released as part of UAVReason, introduced in the paper above.Please cite the paper if you use this dataset.
🧭 Overview
UAVReason Depth… See the full description on the dataset page: https://huggingface.co/datasets/jarvissun/UAVReason_depth.mdm_depth
LingBot-Depth Dataset
Self-curated RGB-D dataset for training LingBot-Depth, a masked depth modeling approach (arxiv:2601.17895). Each sample contains an RGB image, raw sensor depth, and ground truth depth.
Total size: 2.71 TBDepth scale: millimeters (mm), stored as 16-bit PNGLicense: CC BY-NC-SA 4.0
Sub-datasets
Name
Description
Samples
RobbyReal
Real-world indoor scenes captured with multiple RGB-D cameras
1,400,000
RobbyVla
Real-world data… See the full description on the dataset page: https://huggingface.co/datasets/robbyant/mdm_depth.robocasa_cosmos24_success300_env_depthBench2Drive-V0.0.4-depth
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving.
New Features
This is the new 0.0.4 version of Bench2Drive dataset with strictly uniform training data: The new Think2Drive-collected training set (44 scenarios × 25 routes = 1,100 routes) is strictly uniform over scenarios, unlike the previous Base set whose scenario distribution is uneven.
Also introduced new modality: 3D occupancy (3D occ).
This repo only includes the… See the full description on the dataset page: https://huggingface.co/datasets/Bench2DriveData/Bench2Drive-V0.0.4-depth.MultiGen-20M_depth
Dataset Card for "MultiGen-20M_depth"
More Information needed
nyu_depth_v2The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect.BEDLAM2-depth
Dataset Mirror of BEDLAM2.0 Dataset (Depth Data Subset)
Project site: https://bedlam2.is.tuebingen.mpg.de/
Please register at project site for additional information and data in its Download section.
Related Hugging Face dataset mirror: BEDLAM2
Dataset Information
Depth maps (Multilayer EXR, 16-bit, available for 44% of images, 15TB)
Multilayer EXR details
16-bit float depth in red channel (FinalImageMovieRenderQueue_WorldDepth.R)
Color image without motion blur
Body… See the full description on the dataset page: https://huggingface.co/datasets/Intelligent-Systems/BEDLAM2-depth.robocasa-v02-generated300-success-env-depth-rgb-256
RoboCasa v0.2 Generated-300 Success — Env Depth + RGB (256)
Per-step environment depth and RGB sidecars rendered from the original
RoboCasa v0.2 mg_im Generated-300 HDF5 demos, restricted to demos
whose final state matches the task-success predicate. The selection
covers 300 success-audited demos per task across 24 RoboCasa Kitchen
atomic tasks (~7,200 demos total) at native 256-pixel render
resolution. Geometry and RGB are emitted as per-camera contiguous
.npy memmaps designed for… See the full description on the dataset page: https://huggingface.co/datasets/SeonghuJeon/robocasa-v02-generated300-success-env-depth-rgb-256.koch_wrist_cam_depth_112HZ-DepthUAV3DCrop_depth
UAV3DCrop Depth
UAV3DCrop Depth contains per-image depth maps associated with the UAV3DCrop
multi-year agricultural UAV dataset.
Project page: UAV3DCrop
Paper: UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys
Code: UAV3DCrop GitHub
RGB dataset: UAV3DCrop
Repository Contents
The depth data are organized by year and acquisition scene. Scene directories
contain TIFF depth maps named to correspond to images in the main UAV3DCrop… See the full description on the dataset page: https://huggingface.co/datasets/Link-Dev/UAV3DCrop_depth.a-share-l2-market-depth
China A-share Level 2 Market Depth
Canonical order-event and ten-level snapshot data for China A-shares. Canonical
trade records remain in the separate phields/a-share-l2-trades dataset.
Coverage
Date range: 2026-07-24 to 2026-07-24
Trading days: 1
Table
Rows
Parquet files
Compressed size
l2_orders
249,705,486
10
2.14 GiB
l2_snapshots
20,279,887
4
0.91 GiB
Layout… See the full description on the dataset page: https://huggingface.co/datasets/phields/a-share-l2-market-depth.lingbot-depth-subset
Dataset Card for lingbot-depth-subset
This is a FiftyOne dataset with 13,149 samples
(10,207 groups) spanning 3 sub-collections (RobbyReal, RobbyVla, RobbySim).
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/lingbot-depth-subset.mimicgen-aligned-gt-depth
MimicGen Aligned GT Depth Sidecars
Per-frame ground-truth simulator depth for the MimicGen demonstration set,
aligned to the original RGB demo frames. Generated by replaying each demo's
HDF5 simulator states with robosuite + MuJoCo EGL rendering.
Producer: scripts/export_mimicgen_aligned_gt_depth.py in the 3DA_unified
training repo. Original MimicGen data: https://mimicgen.github.io/ (CC BY 4.0).
Layout
26 tasks x ~1000 demos = ~26000 NPZ files, all at the repo root.… See the full description on the dataset page: https://huggingface.co/datasets/SeonghuJeon/mimicgen-aligned-gt-depth.DL3DV-Depth-DA3-Aligned
DL3DV-Depth-DA3-Aligned
Per-frame depth annotations for the DL3DV dataset, produced by
Depth-Anything-3 (DA3) and then aligned to each scene's sparse depth
from the original DL3DV reconstruction. We use this refined dataset for 3D world generation and reconstruction in our Puffin-World.
Sample Videos
Each clip is a 1×3 comparison — RGB | Original Depth | Our Aligned Depth —
with depth rendered by vision banana representation. It shows
how the DA3-aligned depth… See the full description on the dataset page: https://huggingface.co/datasets/KangLiao/DL3DV-Depth-DA3-Aligned.depthtrack_trainDA3-BENCH
DA3-BENCH: Depth Anything 3 Evaluation Benchmark
This repository contains processed benchmark datasets for evaluating Depth Anything 3 depth estimation and visual geometry models. The datasets are provided in a convenient, ready-to-use format for research and evaluation purposes.
About Depth Anything 3
Depth Anything 3 (DA3) is a state-of-the-art model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known… See the full description on the dataset page: https://huggingface.co/datasets/depth-anything/DA3-BENCH.marigold_depth_evalKITTI-Depth-Estimation3D_Visual_Illusion_Depth_Estimation
3D Visual Illusion Depth Estimation Dataset
Dataset Summary
The 3D Visual Illusion Depth Estimation Dataset is designed for research on stereo and monocular depth estimation in 3D visual illusion scenes.It contains left and right stereo images, depth maps estimated from DepthAnything V2, and illusion-region masks.
Dataset Structure
Each sample in the dataset includes:
left: Left-view RGB image
right: Right-view RGB image
depth: Monocularly estimated depth… See the full description on the dataset page: https://huggingface.co/datasets/AdamYao/3D_Visual_Illusion_Depth_Estimation.libero_with_depthThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "panda",
"total_episodes": 1720,
"total_frames": 280007,
"total_tasks": 40,
"total_videos": 0,
"total_chunks": 2,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:1720"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/daixianjie/libero_with_depth.depth_snapshotECCV_Event_Video_Depth_Estimation
Event-Guided Video Depth Estimation Workshop Dataset
This dataset is a mirrored and aligned workshop-ready version of the DVD event-guided video depth estimation data.
It packages each scene into a canonical folder tree that aligns:
low-light RGB frames
per-frame event slices
a scene-level lowlight_event.npz
the matched depth ground truth copied from inference_results/*/normal/depth.npz
The dataset is designed for direct upload to Hugging Face as a dataset repository.
The official… See the full description on the dataset page: https://huggingface.co/datasets/Ethanliang99/ECCV_Event_Video_Depth_Estimation.libero-gt-depth-aligned-hide-sites-fast
LIBERO GT Depth Aligned Hide Sites Fast
This dataset contains ground-truth depth sidecar files for LIBERO demonstrations.
Each episode is stored as a compressed .npz file. The depth is aligned to the original LIBERO HDF5 frames by re-rendering each frame after restoring the simulator state for that timestep.
Contents
episode_*.npz: per-episode depth and camera geometry
manifest_shard_*.jsonl: per-episode metadata, including the source HDF5 path
summary_shard_*.json:… See the full description on the dataset page: https://huggingface.co/datasets/SeonghuJeon/libero-gt-depth-aligned-hide-sites-fast.libero_mujoco3.3.2_depth
LIBERO with depth (MuJoCo 3.3.2 re-render)
The four standard LIBERO benchmark suites,
re-rendered under MuJoCo 3.3.2 and republished in LeRobot v2.1 format with
per-camera metric depth added alongside the usual RGB. no_noops means idle
frames have been stripped.
Contents
This repo holds four independent LeRobot datasets, one per suite:
suite
episodes
frames
tasks
libero_10_no_noops_lerobot
388
104,160
10
libero_goal_no_noops_lerobot
433
52,895
10… See the full description on the dataset page: https://huggingface.co/datasets/flex-pi/libero_mujoco3.3.2_depth.nyu-depthv2-wds
Dataset Card for nyu-depthv2-wds
This is the NYU DepthV2 dataset, converted into the webdataset format. https://huggingface.co/datasets/sayakpaul/nyu_depth_v2/
There are 47584 samples in the training split, and 654 samples in the validation split.
I shuffled both the training samples, and the validation samples.
I also cropped 16 pixels from all sides of the image, and depth image. I did this because there is a white border around all images.
This is an example of the border… See the full description on the dataset page: https://huggingface.co/datasets/adams-story/nyu-depthv2-wds.libero_depth_rlds