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docs/Data_prep.md

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+## ScanNet200
+Download <a href="https://kaldir.vc.in.tum.de/scannet_benchmark/documentation">ScaNnet200</a> and follow <a href="[https://kaldir.vc.in.tum.de/scannet_benchmark/documentation](https://github.com/JonasSchult/Mask3D)">Mask3D</a> to preprocess data. A preprocessed point cloud scene has the following format `id.npy`. Kindly move all preprocessed scenes to their corresponding folders e.g. `0011_00.npy` to `./data/scannet200/scene0011_00`.
+
+ScanNet200 data folder should have the following structure
+```
+./data
+  └── scannet200
+    └── ground_truth
+            ├── scene0011_00.txt
+            └── ...
+    └── scene0011_00
+            ├── poses                            <- folder with camera poses
+            │      ├── 0.txt 
+            │      ├── 1.txt 
+            │      └── ...  
+            ├── color                           <- folder with RGB images
+            │      ├── 0.jpg (or .png/.jpeg)
+            │      ├── 1.jpg (or .png/.jpeg)
+            │      └── ...  
+            ├── depth                           <- folder with depth images
+            │      ├── 0.png (or .jpg/.jpeg)
+            │      ├── 1.png (or .jpg/.jpeg)
+            │      └── ...  
+            ├── intrinsics.txt                 <- camera intrinsics
+            │ 
+            └── 0011_00.npy                <- preprocessed point cloud of the scene
+            │ 
+            └── scene0011_00_vh_clean_2.ply      <- raw point cloud of the scene
+    └── ... 
+```
+
+## Replica
+
+Run the following command. 
+```
+sh scripts/get_replica_dataset.sh
+```
+
+A replica folder with the following format will be generated.
+```
+./data
+  └── replica
+    └── ground_truth
+            ├── office0.txt
+            └── ...
+    └── office0
+            ├── poses                            <- folder with camera poses
+            │      ├── 0.txt 
+            │      ├── 1.txt 
+            │      └── ...  
+            ├── color                           <- folder with RGB images
+            │      ├── 0.jpg (or .png/.jpeg)
+            │      ├── 1.jpg (or .png/.jpeg)
+            │      └── ...  
+            ├── depth                           <- folder with depth images
+            │      ├── 0.png (or .jpg/.jpeg)
+            │      ├── 1.png (or .jpg/.jpeg)
+            │      └── ...  
+            ├── intrinsics.txt                 <- camera intrinsics
+            │ 
+            └── office0_mesh.ply      <- raw point cloud of the scene
+    └── ... 
+```
+

docs/Installation.md

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+## Clone the repository
+```
+git clone https://github.com/aminebdj/OpenYOLO3D.git
+cd OpenYOLO3D
+
+```
+
+## Conda Environment
+```
+export TORCH_CUDA_ARCH_LIST="6.0 6.1 6.2 7.0 7.2 7.5 8.0 8.6"
+
+conda env create -f environment.yml
+
+conda activate openyolo3d
+
+cd models/Mask3D/
+pip3 install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+pip3 install torch-scatter -f https://data.pyg.org/whl/torch-1.12.1+cu113.html
+pip3 install 'git+https://github.com/facebookresearch/detectron2.git@710e7795d0eeadf9def0e7ef957eea13532e34cf' --no-deps
+
+cd third_party
+
+git clone --recursive "https://github.com/NVIDIA/MinkowskiEngine"
+cd MinkowskiEngine
+git checkout 02fc608bea4c0549b0a7b00ca1bf15dee4a0b228
+python setup.py install --force_cuda --blas=openblas
+
+cd ..
+git clone https://github.com/ScanNet/ScanNet.git
+cd ScanNet/Segmentator
+git checkout 3e5726500896748521a6ceb81271b0f5b2c0e7d2
+make
+cd ../../
+
+cd pointnet2
+python setup.py install
+
+cd ../../
+pip3 install pytorch-lightning==1.7.2
+
+pip install black==21.4b2 cloudpickle==3.0.0 future hydra-core==1.0.5 pycocotools>=2.0.2 pydot iopath==0.1.7 loguru albumentations
+
+pip install .
+
+cd ../YOLO-World
+pip install -e .
+cd ../../
+pip install mmyolo==0.6.0 mmdet==3.0.0 plyfile
+pip install openmim
+pip3 install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
+mim install mmcv==2.0.0
+pip install open3d
+pip install pillow==9.1.0
+ 
+
+```
+## Prepare the class agnostic masks
+```
+sh scripts/get_class_agn_masks.sh
+```
+## Prepare the checkpoints
+```
+sh scripts/get_checkpoints.sh
+```