README.md

---
language:
  - en
pretty_name: "MBZUAI Campus Reconstruction Dataset"
tags:
  - 3D reconstruction
  - computer vision
  - NeRF
license: "mit"
task_categories:
  - image-to-3d
  - robotics
  - other

## Dataset Description

description: |
  This dataset provides the necessary files and scripts to reconstruct the MBZUAI campus using COLMAP, GLOMAP, and NERFstudio. It contains preprocessed video sequences and metadata required for hierarchical 3D reconstruction.

  The dataset includes:
  - Raw video sequences
  - Preprocessed frames
  - Calibration and metadata
  - Reconstruction scripts

  The hierarchical reconstruction starts with a base structure, followed by incremental updates with additional sequences.

## Installation

installation: |
  This repository relies on COLMAP, GLOMAP, and NERFstudio. Ensure these dependencies are installed before running the scripts.

## Reproduce Results

steps: |
  1. **Download the Files**
     - Clone the repository and extract the provided dataset folders.

  2. **Process Individual Video Sequences**
     - Run the following command to preprocess the video sequences:
       ```bash
       python prepare_data.py
       ```

  3. **Reconstruct the Campus Hierarchically**
     - Run the `core.sh` script to reconstruct the base structure:
       ```bash
       ./core.sh
       ```
     - For additional scenes, copy and modify `run_template.sh` to match the scene name and file, then execute it step-by-step or as a whole.

## Dataset Purpose

use_cases: |
  This dataset is essential for:
  - 3D environment reconstruction
  - Research on NeRF-based scene reconstruction
  - Structure-from-Motion experiments
  - Hierarchical multi-view scene understanding
---

# This is the repository to download and prepare the MBZUAI campus reconstruction

## Installation 

TL,DR --> run install.sh (if it's there)

Conda environment

```bash 
conda create --name nerfstudio -y python=3.8
conda activate nerfstudio
pip install --upgrade pip
```

CUDA Dependencies

```bash
pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118

conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
```

NERFstudio install

```bash
pip install nerfstudio
```

COLMAP install

```bash
conda install -y conda-forge::colmap
```

GLOMAP install

```bash
conda install -y conda-forge::glomap
```

ffmpeg for processing images

```bash
conda install conda-forge::ffmpeg
```

## Reproduce results

### 1. Pre-Process individual videos sequences

```bash
python prepare_data.py
```

### 2. Reconstruct the campus hierarchically

first run the `run_core.sh` script. This will reconstruct a base structure, upon which all the other reconstructions will be added to.
This is the most important one.

For the other scenes, for now just copy the run_template.sh file and modify according to the current scane name and file.
Usually I run line by line to make sure everything is correct, but theoretically you could just run the entire file.