Datasets:

rafiibnsultan
/

PAVE

language:
  - en
license: cc-by-4.0
size_categories:
  - 10K<n<100K
task_categories:
  - image-text-to-text
  - image-segmentation
  - depth-estimation
pretty_name: 'PAVE: Pedestrian Accessibility Vision–Language Dataset'
tags:
  - vision-language
  - multimodal
  - grounded
  - spatial-reasoning
  - accessibility
  - navigation
  - segmentation
  - depth-aware
  - VQA
  - pedestrian
  - CVPR-2026
  - real-world
  - grounded-conversation
  - urban-scenes

PAVE: Pedestrian Accessibility and Visual-grounded Evaluation

PAVE is a structured vision–language dataset introduced in the CVPR 2026 paper:

WalkGPT: Grounded Vision–Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation
(Accepted at CVPR 2026)
Paper | Code | Project Page

PAVE is a spatially grounded VQA benchmark for accessibility-aware reasoning in real-world pedestrian environments, unifying language understanding, pixel-level grounding, and depth-aware navigation guidance.

Overview

PAVE provides structured annotations built on top of the publicly available SANPO dataset from Google Research:

https://github.com/google-research-datasets/sanpo_dataset

We sincerely thank the SANPO authors for making their dataset publicly available.
PAVE does not redistribute SANPO images. Instead, it provides:

Question–Answer pairs
Accessibility reasoning annotations
Segmentation references
Distance annotations
Structured grounding tags

Users must independently download SANPO under its original license to use PAVE.

Dataset Structure

The repository contains:

PAVE.jsonl — Full dataset
PAVE_train.jsonl — Official train split
PAVE_val.jsonl — Official validation split
PAVE_train85.jsonl — Session-balanced training subset (85 sessions)
PAVE_val85.jsonl — Session-balanced evaluation subset (6 sessions)
PAVE_labelmap.json — Ontology and accessibility label definitions

Official Splits

To reduce redundancy while preserving scene-level diversity, we uniformly sample 100 frames per session.

85 sessions used for training (~8.5k frames)
6 sessions held out for evaluation (~600 frames)

These splits correspond to the setup described in the WalkGPT paper.

For full training, use:

PAVE_train.jsonl
PAVE_val.jsonl

For session-balanced experiments (as in the paper):

PAVE_train85.jsonl
PAVE_val85.jsonl

Annotation Format

Each JSON entry contains:

Reference to SANPO image file
Question
Structured reasoning output
Segmentation grounding tags [p] ... [/p][SEG]
Distance annotations [distance] ... [/distance]
Accessibility assessment tags [assessment] ... [/assessment]

The dataset is designed to evaluate:

Grounded multimodal reasoning
Accessibility-aware scene understanding
Depth-aware spatial interpretation
Structured segmentation-language alignment

Accessibility Ontology

The file PAVE_labelmap.json defines:

Semantic classes
Mapping of classes to accessibility categories
Definitions of accessible vs non-accessible elements

Accessibility categories are defined from a pedestrian navigation perspective as described in the WalkGPT paper.

For detailed ontology design and evaluation protocol, please refer to the paper.

Relationship to SANPO

PAVE annotations are derived from the SANPO dataset.

Images, depth maps, and base masks are not redistributed here.
Users must obtain SANPO independently.
PAVE provides annotation layers on top of SANPO.

Please cite SANPO when using the underlying images.

Intended Use

PAVE is intended for:

Research in grounded vision–language models
Accessibility-aware navigation systems
Spatial reasoning benchmarks
Multimodal segmentation research

For a detailed discussion of limitations and ethical considerations, see the WalkGPT paper.

License

PAVE annotations are released under:

Creative Commons Attribution 4.0 (CC BY 4.0)

This license applies to the annotation files only.
SANPO images are governed by their original license.

Citation

If you use PAVE, please cite:

@inproceedings{walkgpt2026,
  title={WalkGPT: Grounded Vision–Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation},
  author={Rafi Ibn Sultan, Hui Zhu, Xiangyu Zhou, Chengyin Li, Prashant Khanduri, Marco Brocanelli, Dongxiao Zhu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}