dataset_NEXTLIFE - version1

NEXTLIFE — 3D Objects and Distortions

A dataset for studying how 3D object distortions affect object recognition and visual security.

• 119 retained reference objects, with their materials and textures.
• 3,332 distorted variants, corresponding to 28 profiles per object.
• 5 experimental environments.
• 6 archive volumes to download, approximately 13.8 GiB in total.

The transformations include mesh simplification and quantization, texture resizing, compression and blurring, as well as encryption and obscuration. The mesh “data hiding” profile currently uses an encryption-based proxy.

1. Download contents

The assets are provided as three separate ZIP archives, split into volumes of at most 4 GiB. All three archives have passed 7-Zip integrity checks and file-inventory verification.

Reference objects — one volume

• NEXTLIFE_20260930_source.zip.001

Distorted variants — four volumes

• NEXTLIFE_20260930_distorted.zip.001
• NEXTLIFE_20260930_distorted.zip.002
• NEXTLIFE_20260930_distorted.zip.003
• NEXTLIFE_20260930_distorted.zip.004

Experimental environments — one volume

• NEXTLIFE_20260930_scenes.zip.001

Download every volume and keep all original filenames. Place the volumes together in one download folder. Use 7-Zip and start extraction from .zip.001 only: the remaining volumes are read automatically. Do not extract the parts separately or rename them to .zip.

The download also includes:

• README_EXTRACT.txt — installation and extraction instructions.
• dataset_info.json — archive inventory, volume sizes and checksums.
• SHA256SUMS.txt — SHA-256 checksums for checking the downloaded volumes.

2. Folder structure

After extraction, the assets follow the repository folder structure:

Objects/
├── Originals/              Reference objects, materials and textures
└── Distorted/
    ├── MeshVariants/       Transformed meshes
    ├── TextureVariants/    Transformed textures
    ├── CombinedVariants/   Manifests linking meshes and textures
    └── dataset_info.json   Validated selection and dataset identifier
Scenes/                    Five active experimental environments

Keep the complete folder structure unchanged. A variant manifest references other files; it is not a standalone 3D object. Excluded reference objects are not included in this distribution.

Objects/Distorted/dataset_info.json preserves the validated object selection and the stable dataset identifier. It is different from the dataset_info.json supplied beside the archives, which describes the download itself.

3. Code and tools

The 3D assets are distributed separately from the code. The viewers, scripts, configuration files and metadata needed to run the experiments are available in the NEXTLIFE GitHub repository.

The tools allow users to explore objects, compare distorted variants and run an object recognition and visual security experiment. The distortions are already generated: no regeneration is required to use this download.

4. Installation on Windows

Step 1 — Prepare the tools

Install Git, Python 3.12 and 7-Zip. Open PowerShell in a working directory and run these commands, one line at a time:

git clone https://github.com/Kaldrass/Dataset_NEXTLIFE.git
cd Dataset_NEXTLIFE
python -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt

Step 2 — Extract the assets

Download all six archive volumes into one folder, for example D:\Downloads\NEXTLIFE. From the root of the cloned repository, run the following commands, adapting the download path:

$assets = "D:\Downloads\NEXTLIFE"
& "$env:ProgramFiles\7-Zip\7z.exe" x "$assets\NEXTLIFE_20260930_source.zip.001" -o.
& "$env:ProgramFiles\7-Zip\7z.exe" x "$assets\NEXTLIFE_20260930_distorted.zip.001" -o.
& "$env:ProgramFiles\7-Zip\7z.exe" x "$assets\NEXTLIFE_20260930_scenes.zip.001" -o.

These commands assume that 7-Zip is installed in its standard Windows location. Alternatively, open each .zip.001 file in the 7-Zip application, choose Extract, and select the root of the cloned repository as the destination.

Extract into a fresh clone to avoid mixing asset versions. The archives create Objects/Originals/, Objects/Distorted/ and Scenes/ directly beside metadata.json and ExperimentSecurity/. Do not extract into an additional Objects/ folder.

Allow enough disk space for both the downloaded archives and approximately 50 GiB of extracted assets.

Step 3 — Verify the download

Calculate the SHA-256 checksum of each volume and compare it with the corresponding entry in SHA256SUMS.txt. For example:

Get-FileHash "$assets\NEXTLIFE_20260930_source.zip.001" -Algorithm SHA256

Repeat this check for the other five volumes.

Linux and macOS

With the 7-Zip command-line tool installed, extract from the repository root using the following commands. Adapt the download path:

7zz x /path/to/downloads/NEXTLIFE_20260930_source.zip.001 -o.
7zz x /path/to/downloads/NEXTLIFE_20260930_distorted.zip.001 -o.
7zz x /path/to/downloads/NEXTLIFE_20260930_scenes.zip.001 -o.

For the Python commands in this guide, create the environment with python3 -m venv .venv and use .venv/bin/python instead of .\.venv\Scripts\python.exe. Use forward slashes in script paths.

5. Explore the dataset and run a demonstration

Build the catalog

From the repository root:

.\.venv\Scripts\python.exe ExperimentSecurity\build_dataset_catalog.py

Prepare a small trial set

This command prepares a demonstration with up to 20 trials and 5 distinct objects:

.\.venv\Scripts\python.exe ExperimentSecurity\build_recognition_experiment.py --max-objects 5 --max-trials 20 --max-faces 0 --choices 6 --seed 20260622

This replaces recognition_trials.json. Back up any existing trial set before changing it. The demonstration checks that the tools work; it is not a balanced experimental design.

Start the local server

.\.venv\Scripts\python.exe -m http.server 8015 --bind 127.0.0.1

Keep the terminal open, then open one of these addresses in Chrome:

• Dataset explorer: compare reference objects and distorted variants.
• Participant experiment: run the recognition and visual security task.

These links work on the computer running the local server. The viewers load Three.js from the Internet. If the scenes are missing, a fallback environment is used: obtain the intended scenes before running an actual study. To stop the server, press Ctrl + C in the terminal.

6. Export results

1. For each trial, choose an object label and a visual security level, then click “Valider” (Validate).
2. After validating the final trial, export the responses using the JSON or CSV button.
3. Name each export using an anonymous participant identifier.
4. Export the current session before using Reset for the next participant.

Responses are stored in the browser and are not automatically sent to the server.

Analysis commands and detailed usage instructions are available in the ExperimentSecurity documentation on GitHub.

ExperimentDSIS/ contains a separate historical experiment. The Old/ archives, temporary files and earlier generations are not needed to use these assets.