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We detail data download and release instructions for preprocessing with scripts for ScanNet + 3RScan.
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| │ ├── objectsDataMultimodal.pt -> object data combined from data1D.pt + data2D.pt + data3D.pt (for easier loading)
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| │ └── sel_cams_on_mesh.png (visualisation of the cameras selected for computing RGB features per scan)
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| └── ...
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```
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### ARKitScenes
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#### Running preprocessing scripts
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Adjust the path parameters of `ARKitScenes` in the config files under `configs/preprocess`. Run the following (after changing the `--config-path` in the bash file):
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```bash
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$ bash scripts/preprocess/process_arkit.sh
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```
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Our script for ARKitScenes dataset performs the following additional processing:
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- 3D-to-2D projection for 2D segmentation and stores as `gt-projection-seg.pt` for each scan.
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Post running preprocessing, the data structure should look like the following:
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```
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ARKitScenes/
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├── objects_chunked/ (object data chunked into hdf5 format for instance baseline training)
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| ├── train_objects.h5
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| └── val_objects.h5
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├── scans/
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| ├── 40753679/
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| │ ├── gt-projection-seg.pt -> 3D-to-2D projected data consisting of framewise 2D instance segmentation
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| │ ├── data1D.pt -> all 1D data + encoded (object referrals + BLIP features)
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| │ ├── data2D.pt -> all 2D data + encoded (RGB + floorplan + DinoV2 features)
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| │ ├── data2D_all_images.pt (RGB features of every image of every scan )
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| │ ├── data3D.pt -> all 3D data + encoded (Point Cloud + I2PMAE features - object only)
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| │ ├── object_id_to_label_id_map.pt -> Instance ID to NYU40 Label mapped
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| │ ├── objectsDataMultimodal.pt -> object data combined from data1D.pt + data2D.pt + data3D.pt (for easier loading)
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| │ └── sel_cams_on_mesh.png (visualisation of the cameras selected for computing RGB features per scan)
Adjust path/configuration parameters in `configs/train/train_scene_crossover.yaml`. You can also add your customised dataset or choose to train on Scannet & 3RScan or either. Run the following:
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Adjust path/configuration parameters in `configs/train/train_scene_crossover.yaml`. You can also add your customised dataset or choose to train on Scannet, 3RScan & ARKitScenes or any combination of the same. Run the following:
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