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Data preparation

Since this code is based on ScanRefer, you can use the same 3D features. Please also refer to the ScanRefer data preparation.

  1. Download the ScanQA dataset under data/qa/.

    Dataset format

    "scene_id": [ScanNet scene id, e.g. "scene0000_00"],
"object_id": [ScanNet object ids (corresponds to "objectId" in ScanNet aggregation file), e.g. "[8]"],
"object_names": [ScanNet object names (corresponds to "label" in ScanNet aggregation file), e.g. ["cabinet"]],
"question_id": [...],
"question": [...],
"answers": [...],

  2. Download the preprocessed GLoVE embedding and put them under data/.

  3. Download the ScanNetV2 dataset and put (or link) scans/ under (or to) data/scannet/scans/ (Please follow the ScanNet Instructions for downloading the ScanNet dataset).

  4. Pre-process ScanNet data. A folder named scannet_data/ will be generated under data/scannet/ after running the following command:

    cd data/scannet/
python batch_load_scannet_data.py

  1. (Optional) Pre-process the multiview features from ENet.

    a. Download the ENet pretrained weights and put it under data/

    b. Download and unzip the extracted ScanNet frames under data/

    c. Change the data paths in config.py marked with TODO accordingly.

    d. Extract the ENet features:

    python scripts/compute_multiview_features.py

    e. Project ENet features from ScanNet frames to point clouds:

    python scripts/project_multiview_features.py --maxpool