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.gitignore

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+# Byte-compiled / optimized / DLL files
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DATASETS.md

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+# Datasets
+
+For training, we mainly use [RealEstate10K](https://google.github.io/realestate10k/index.html), [DL3DV](https://github.com/DL3DV-10K/Dataset), and [ACID](https://infinite-nature.github.io/) 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](https://github.com/dcharatan/pixelsplat) and [MVSplat](https://github.com/donydchen/mvsplat) to train and evaluate on 256x256 resolution.
+
+Please refer to [here](https://github.com/dcharatan/pixelsplat?tab=readme-ov-file#acquiring-datasets) for acquiring the processed 360p dataset (360x640 resolution).
+
+If you would like to train and evaluate on the high-resolution RealEstate10K dataset, you will need to download the 720p (720x1280) version. Please refer to [here](https://github.com/yilundu/cross_attention_renderer/tree/master/data_download) for the downloading script. Note that the script by default downloads the 360p videos, you will need to modify the`360p` to `720p` in [this line of code](https://github.com/yilundu/cross_attention_renderer/blob/master/data_download/generate_realestate.py#L137) to download the 720p videos.
+
+After downloading the 720p dataset, you can use the scripts [here](https://github.com/dcharatan/real_estate_10k_tools/tree/main/src) to convert the dataset to the desired format in this codebase.
+
+
+
+## DL3DV
+
+In the DL3DV experiments, we trained with RealEstate10k at 256x256, 512x512 and 368x640 resolutions, respectively.
+
+For the training set, we use the [DL3DV-480p](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P) dataset (270x480 resolution), where the 140 scenes in the test set are excluded during processing the training set. After downloading the [DL3DV-480p](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P) dataset, you can then use the script [src/scripts/convert_dl3dv.py](src/scripts/convert_dl3dv.py) to convert the training set.
+
+Please note that you will need to update the dataset paths in the aforementioned processing scripts.
+
+If you would like to train on the high-resolution DL3DV dataset, you will need to download the [DL3DV-960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) version (540x960 resolution). Simply follow the same procedure for data processing (use the `images_4` folder instead of `images_8`).
+
+
+
+## Additional Datasets
+We also test our method on DTU and ScanNet++ datasets for novel view synthesis, and ScanNet-1500 for pose estimation. We will provide the download link later.
+
+If you would like to train and/or evaluate on additional datasets, just modify the [data processing scripts](src/scripts) to convert the dataset format. Kindly note the [camera conventions](https://github.com/cvg/depthsplat/tree/main?tab=readme-ov-file#camera-conventions) used in this codebase.

LICENSE

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+MIT License
+
+Copyright (c) 2024 Botao Ye
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.