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LLaVA-Next-3D/README.md

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+## Installation
+
+1. Clone this repository:
+```bash
+git clone https://github.com/ZCMax/LLaVA-Next-3D.git
+cd LLaVA-Next-3D
+```
+
+2. Create the conda environment:
+```bash
+conda create -n llavanext3d python=3.10 -y
+conda activate llavanext3d
+pip install --upgrade pip  # Enable PEP 660 support.
+pip install -e ".[train]"
+pip install flash-attn --no-build-isolation     # install flash attention
+```
+
+
+## Data Preparation
+
+The directory should be orgainized as:
+
+```
+LLaVA-3D-Next # project root
+├── data
+│   ├── scannet
+│   │   ├── scans
+│   │   ├── posed_images
+│   │   ├── pcd_with_object_aabbs
+│   │   └── mask
+│   ├── embodiedscan
+│   │   ├── embodiedscan_infos_full_llava3d_v2.json
+│   ├── metadata
+│   │   ├── scannet_select_frames.json
+│   │   ├── pcd_discrete_0.1.pkl
+│   │   ├── scannet_train_gt_box.json
+│   │   └── scannet_val_pred_box.json
+│   ├── prcoessed
+│   │   ├── multi3drefer_train_llava_style.json
+│   │   ├── multi3drefer_val_llava_style.json
+│   │   ├── ...
+```
+
+We have prepared the well organzied data under `/mnt/hwfile/openmmlab/zhuchenming/llava-next-3d-data`, you can directly link this to your data. Currently we only support training on ScanNet.
+
+
+## Training & Inference
+### Full-finetuned Training
+
+You can use sbatch to launch the multi-node script:
+
+```bash
+sh scripts/3d/train/train_16gpu_sbatch.sh
+```
+
+### Inference
+```bash
+sh scripts/3d/eval/eval_scanrefer.sh $CKPT_NAME uniform 32
+```