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Datasets

Training datasets

For training, we mainly use RealEstate10K and DL3DV datasets. We provide the data processing scripts to convert the original datasets to pytorch chunk files which can be directly loaded with this codebase.

Expected folder structure:

├── datasets
│   ├── re10k
│   ├── ├── train
│   ├── ├── ├── 000000.torch
│   ├── ├── ├── ...
│   ├── ├── ├── index.json
│   ├── ├── test
│   ├── ├── ├── 000000.torch
│   ├── ├── ├── ...
│   ├── ├── ├── index.json
│   ├── dl3dv
│   ├── ├── train
│   ├── ├── ├── 000000.torch
│   ├── ├── ├── ...
│   ├── ├── ├── index.json
│   ├── ├── test
│   ├── ├── ├── 000000.torch
│   ├── ├── ├── ...
│   ├── ├── ├── index.json

By default, we assume the datasets are placed in datasets/re10k, datasets/dl3dv, and datasets/acid. Otherwise you will need to specify your dataset path with dataset.DATASET_NAME.roots=[YOUR_DATASET_PATH] in the running script.

We also provide instructions to convert additional datasets to the desired format.

RealEstate10K

For experiments on RealEstate10K, we primarily follow pixelSplat and MVSplat to train and evaluate on 256x256 resolution.

Please refer to here for acquiring the processed 360p dataset (360x640 resolution).

DL3DV

In the DL3DV experiments, we trained with RealEstate10k at 256x256 resolution.

For the training set, we use the DL3DV-480p dataset (270x480 resolution), where the 140 scenes in the test set are excluded during processing the training set. After downloading the DL3DV-480p dataset, You can first use the script src/scripts/convert_dl3dv_train.py to convert the training set, and then run src/scripts/generate_dl3dv_index.py to generate the index.json file for the training set.

Please note that you will need to update the dataset paths in the aforementioned processing scripts.