Datasets:

harvardairobotics
/

VISTA

license: cc-by-4.0
language:
  - en
pretty_name: VISTA
task_categories:
  - visual-question-answering
  - text-generation
tags:
  - video
  - audio
  - timeseries
  - 3d
  - egocentric
  - multimodal
  - accessibility
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: validation
        path: val.csv
      - split: test
        path: test.csv

VISTA: A Multimodal Egocentric Benchmark for Goal-Oriented Perception and Assistance to Blind and Low-Vision Users

Dataset Summary

VISTA (Visually Impaired Scene and Task Annotations) is a multimodal egocentric benchmark for goal-oriented perception and assistance to blind and low-vision users. It contains 997 first-person video clips spanning ten assistive task categories, with 1,063 scene descriptions, 1,191 question–answer pairs, and 2,067 goal–instruction pairs.

The dataset pairs RGB video with SLAM-camera and eye-camera recordings, inertial and environmental measurements, gaze, hand-tracking, and trajectory signals. Spoken task requests accompany the annotations. Clips are organized into 497 training, 50 validation, and 450 test examples, with no clip overlap between splits.

Supported Tasks

Scene description: describe objects, layout, obstacles, and hazards in the first-person view.
Visual question answering: answer questions about the observed scene.
Goal-conditioned guidance: provide instructions for achieving an assistive goal.

Languages

English.

Quick Start

The default Hub configuration exposes the annotation CSVs as train, validation, and test tables. Each row represents one clip; media and sensor files are located using its clip_id.

For an already downloaded dataset:

import csv
from pathlib import Path

root = Path("/path/to/VISTA")
with (root / "test.csv").open(encoding="utf-8-sig", newline="") as handle:
    sample = next(csv.DictReader(handle))

clip_id = sample["clip_id"]
clip = root / "test" / clip_id.split("_")[0] / clip_id
rgb_video = clip / f"{clip_id}.mp4"
slam_left = clip / "vrs" / "slam_left.mp4"
question_audio = clip / "generated-audio" / "q1.wav"

Use the question or goal index corresponding to the requested annotation when selecting its WAV.

Splits and annotations

Split	Clips	Scene-description annotations	Question–answer pairs	Goal–instruction pairs	Generated task WAVs
Train	497	555	622	1,091	2,210
Validation	50	52	59	108	217
Test	450	456	510	868	1,828
Total	997	1,063	1,191	2,067	4,255

Clip IDs are disjoint across splits. Counts are based on the supplied CSVs. A clip can have multiple reference annotations, so annotation totals are not counts of independent videos. Each clip has one scene-request WAV, regardless of its number of scene-description references.

Task categories

ID	Category	Clips
TC01	Object localization	100
TC02	Hazard detection	99
TC03	Scene / situational awareness	102
TC04	Navigation	101
TC05	Social / person description, including facial-expression tasks	97
TC06	Distance estimation	99
TC07	Tool use / task instructions	100
TC08	Object identification	100
TC09	Reading / text recognition	102
TC10	Motion / dynamic scene understanding	97

File structure

The dataset repository root contains the three annotation CSVs and their corresponding media folders:

VISTA/
├── README.md
├── train.csv
├── val.csv
├── test.csv
├── train/
│   └── TC01/
│       └── TC01_002/
│           ├── TC01_002.mp4
│           ├── generated-audio/
│           │   ├── q1.wav
│           │   ├── g1.wav
│           │   └── scene-discription-prompt.wav
│           ├── vrs/
│           │   ├── slam_left.mp4
│           │   ├── slam_right.mp4
│           │   ├── eye.mp4
│           │   ├── imu_left.csv
│           │   ├── imu_right.csv
│           │   ├── magnetometer.csv
│           │   ├── barometer.csv
│           │   ├── rgb_timestamps.csv
│           │   └── time_domain_mapping.csv
│           └── mps/
│               ├── eye_gaze/
│               │   └── general_eye_gaze.csv
│               ├── hand_tracking/
│               │   └── hand_tracking_results.csv
│               └── slam/
│                   ├── closed_loop_trajectory.csv
│                   ├── open_loop_trajectory.csv
│                   ├── online_calibration.jsonl
│                   ├── semidense_points.csv.gz
│                   └── semidense_observations.csv.gz
├── val/
│   └── TC01/ ... TC10/
└── test/
    └── TC01/ ... TC10/

This is a representative layout, not a complete listing of supplementary metadata files. The number of qN.wav and gN.wav files varies with the annotations.

Annotation schema

Each CSV row represents one clip, identified by clip_id.

scene_description_N: a reference scene description.
question_N and answer_N: a question and its paired reference answer.
goal_N and instruction_N: a goal and its paired reference instruction.

Empty cells represent absent annotations. Preserve the original indices when matching annotations to audio; do not renumber populated columns after dropping empty cells.

Known Limitations

Geographic coverage: Recordings were collected in Boston, so the dataset may not represent environments and assistive scenarios in other locations.
Missing tracking data: Hand-tracking results are missing for 15 clips, and derived SLAM data (trajectories, point clouds, observations, and calibration) for 11 different clips. In total, 26 clips lack at least one derived modality. RGB, camera videos, gaze, inertial/environmental measurements, and generated task audio are available for all 997 clips.
Safety-critical use: Benchmark performance does not establish that a model is safe for real-world navigation or assistance. Deployment requires evaluation with blind and low-vision users.

Licensing Information

The dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). See the license terms. The accompanying benchmark code has a separate MIT license.