datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ViewSpatial-Bench
ViewSpatial-Bench: Evaluating Multi-perspective Spatial Localization in Vision-Language Models
Dataset Description
We introduce ViewSpatial-Bench, a comprehensive benchmark with over 5,700 question-answer pairs across 1,000+ 3D scenes from ScanNet and MS-COCO validation sets. This benchmark evaluates VLMs' spatial localization capabilities from multiple perspectives, specifically testing both egocentric (camera) and allocentric (human subject) viewpoints across… See the full description on the dataset page: https://huggingface.co/datasets/lidingm/ViewSpatial-Bench.LiDAR-LLM-Nu-Caption
Dataset Details
Dataset type:
This is the nu-Caption dataset, a QA dataset designed for training MLLM models on caption tasks in autonomous driving scenarios. It is built upon the NuScenes dataset.
Dataset keys:
"answer" is the output of the VLM models using image data. "answer_lidar" uses GPT4O-mini to filter information that cannot be obtained from the image data.
If you want to train the model like LiDAR-LLM, which only uses the LiDAR modality and does not use the vision modality… See the full description on the dataset page: https://huggingface.co/datasets/Senqiao/LiDAR-LLM-Nu-Caption.LiDAR-LLM-Nu-Grounding
Dataset Details
Dataset type:
This is the nu-Grounding dataset, a QA dataset designed for training MLLM models for grounding in autonomous driving scenarios. This QA dataset is built upon the NuScenes dataset.
Where to send questions or comments about the dataset:
https://github.com/Yangsenqiao/LiDAR-LLM
Project Page:
https://sites.google.com/view/lidar-llm
Paper:
https://arxiv.org/abs/2312.14074
douvras-lidar-risk-synthetic
Douvras LiDAR Risk Synthetic v0.1
Benchmark tabular sintético de risco em corredores LiDAR. Cada linha representa
estatísticas resumidas de uma cena (clearance, densidade de pontos, vegetação e fios) e
um rótulo low, attention, warning ou critical produzido por uma regra explícita.
As cenas são disjuntas entre train, validation e test (36/12/12 registros). Não há
imagens aéreas, nuvens de pontos de clientes ou dados TTPLA neste release. O benchmark
serve para validar o pipeline… See the full description on the dataset page: https://huggingface.co/datasets/dougdotcon/douvras-lidar-risk-synthetic.minuszero-indian-autonomous-driving-multicam-lidar
Minus Zero Indian Urban Autonomous Driving Dataset - Multicamera LiDAR
Overview
This dataset provides original multicamera autonomous-driving recordings with LiDAR in MCAP format. It is designed for non-commercial research on camera and LiDAR perception, sensor synchronization, H.265 video pipelines, localization, GNSS/pose integration, and robotics data tooling.
The dataset is public for personal, educational, and research use under CC BY-NC 4.0. Commercial use… See the full description on the dataset page: https://huggingface.co/datasets/gagandeepreehal/minuszero-indian-autonomous-driving-multicam-lidar.naf-style-lidc-idri-0001-chest
Scitomo reproducible NAF-style LIDC-IDRI-0001 Chest reference
This package is a Scitomo-prepared derivative of the exact TCIA LIDC-IDRI series selected by the later official R2-Gaussian synthetic-data workflow. It is intended for a separately named reproducible NAF-style simulation.
Source authority
TCIA collection: LIDC-IDRI
Patient: LIDC-IDRI-0001
StudyInstanceUID: 1.3.6.1.4.1.14519.5.2.1.6279.6001.298806137288633453246975630178
SeriesInstanceUID:… See the full description on the dataset page: https://huggingface.co/datasets/scitomo/naf-style-lidc-idri-0001-chest.LIDQ
license: apache-2.0
License
apache-2.0
SpatialEvo-160K
SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments
SpatialEvo-160K
Dataset Description
SpatialEvo-160K is an offline spatial reasoning QA dataset generated by the Deterministic Geometric Environment (DGE) from SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments. This dataset is not used in the SpatialEvo training pipeline reported in the paper; it is released… See the full description on the dataset page: https://huggingface.co/datasets/lidingm/SpatialEvo-160K.franka-insert-siemens-lid-eval-normal-50franka-insert-siemens-lid-eval-ood-2-20franka-insert-siemens-lid-eval-ood-3-5franka-insert-siemens-lid-eval-ood-1-20franka-insert-siemens-lid-eval-6dof-7mytest