WP3-T3.2-3D-model-descriptors-extraction

This component does the 3D model descriptor extraction task for XRECO Project. XRECO is an HorizonEurope Innovation Project co-financed by the EC under Grant Agreement ID: 101070250.

It implements an analysis service to extract descriptors for 3D models. Once a request job has been created, the results can be polled via a job status query.

Installation

The docker and python code have been tested in Ubuntu 20.04 and python 3.10.

To build the docker type the following command

docker build -t multimodal_image .

And to run the docker type the following command

docker run -ti --rm --gpus all --network host -p 8000:8000 multimodal_image

Service API

The service API can be seen on http://localhost:8000/3D model retrieval/

Extracting descriptors for a 3D model

For extracting the descriptors of a 3D model only the file path (accessible by the server), minIO, or S3 URL ("data") of the 3D model has to be specified. Please note as of v1.0.0 of the service only GLTF files are supported.

curl -X 'POST' \
  'http://localhost:8000/3D model retrieval/extract/airplane_0627' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "data": "https://xreco-nmr.s3.eu-west-1.amazonaws.com/test/airplane_0627.gltf",
  "start": 0,
  "end": 0,
  "last": true
}'

Result JSON data:

[
"{\"jobId\": \"741\"}"
]

Get the descriptor of the 3D model

You can then get the extracted descriptor by calling the 2nd endpoint of the API using the feature name and job ID:

curl -X 'GET' \
  'http://localhost:8000/3D model retrieval/extract/airplane_0627/741' \
  -H 'accept: application/json'

Result JSON data:

[
"{\"status\": \"completed\", \"result\": [0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0]}"
]

Authors

Maria Pegia Email: mpegia@iti.gr

Stamatis Samaras Email: sstamatis@iti.gr

Licence

MIT Licence

Acknowledgement

This work has received funding from the European Union’s Horizon Europe Research and Innovation Programme under grant agreement No 101070250 XReco