πŸ“Š
PanoEnv dataset by 7zkk
⭐ 52.1
πŸ“Š
Dataset

PanoEnv

by 7zkk 7zkk/panoenv
Free2AITools Nexus Index
52.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 33
P: Popularity 52
R: Recency 76
Q: Quality 50
Tech Context
Vital Performance —
Source β†’
Data Integrity 52.1 FNI Score
- Size
- Rows
- Tokens
Dataset Information Summary
Entity Passport
Registry ID 7zkk/panoenv
License CC-BY-4.0
Provider huggingface
πŸ“œ

Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_dataset_7zkk_panoenv,
  author = {7zkk},
  title = {PanoEnv Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/7zkk/PanoEnv}},
  note = {Accessed via Free2AITools.}
}
APA Style
7zkk. (2026). PanoEnv [Dataset]. Free2AITools. https://huggingface.co/datasets/7zkk/PanoEnv

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 33
Popularity (P) 52
Recency (R) 76
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for PanoEnv: Authority (A:33), Popularity (P:52), Recency (R:76), Quality (Q:50). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
⬇️
Downloads
34,867

🎯 Task Categories

visual-question-answering image-text-to-text

πŸ‘οΈ Data Preview

πŸ“Š

Row-level preview not available for this dataset.

Schema structure is shown in the Field Logic panel when available.

πŸ”— Explore Full Dataset β†—

🧬 Field Logic

🧬

Schema not yet indexed for this dataset.

Dataset Specification

PanoEnv-QA: A Large-Scale Geometry-Grounded Panoramic VQA Benchmark for 3D Spatial Intelligence

πŸ“– Overview

PanoEnv-QA is a large-scale Visual Question Answering benchmark designed specifically to probe 3D spatial intelligence on Equirectangular Projection (ERP) panoramas. Built from synthetic but photorealistic 3D environments (TartanAir), PanoEnv-QA offers over 14.8K questions spanning five categories that progressively require stronger 3D understandingβ€”all grounded in precise 3D annotations (depth, semantics, and 3D bounding boxes).

Key Features

  • Geometry-Grounded: All QA pairs are programmatically derived from physical ground truth (depth maps, semantic segmentation, 3D bounding boxes)
  • 360Β° Panoramic: Targets the unique challenges of ERP images including geometric distortions and multi-view reasoning
  • RL-Ready: Designed to serve both as a reliable evaluation benchmark and as a source of verifiable supervision signals for reinforcement learning
  • Diverse & Balanced: 60 diverse environments with balanced question distribution across 5 major categories

πŸ“Š Dataset Statistics

Split Environments Images QA Pairs
Train 60 415 10,340
Val 60 60 1,496
Test 60 120 2,991
Total 60 595 14,827

Question Distribution

| Major Category | # Questions | Percentage

Social Proof

HuggingFace Hub
34.9KDownloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Dataset Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-dataset--7zkk--panoenv
slug
7zkk--panoenv
source
huggingface
author
7zkk
license
CC-BY-4.0
tags
task_categories:visual-question-answering, task_categories:image-text-to-text, language:en, license:cc-by-4.0, size_categories:1k<n<10k, format:imagefolder, modality:image, library:datasets, library:mlcroissant, arxiv:2602.21992, region:us, 3d-scene-understanding, spatial-reasoning, multi-view, vqa, panorama, 360-degree, equirectangular

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
34,867
stars
null
forks
null

Data indexed from public sources. Updated daily.