{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# \ud83e\uddb7 OralRevolution 3D Gaussian Splatting (3DGS) Pipeline\n",
        "### 100% Free Cloud GPU Training (Luma AI Quality)\n",
        "This notebook is specially configured to train authentic, photorealistic 3D Gaussian Splats from frames exported by OralRevolution Studio.\n",
        "\n",
        "**Instructions:**\n",
        "1. Run **Step 1: Install 3DGS & Compiling CUDA Submodules** (takes ~1-2 min to compile CUDA).\n",
        "2. Run **Step 2: Upload Your ZIP File** (shows a direct Choose Files button).\n",
        "3. Run **Step 3: Run COLMAP & 3D Gaussian Splat Training**.\n",
        "4. Run **Step 4: Download Your Trained .PLY** and drag it straight into OralRevolution Studio!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": [
        "# @title Step 1: Install 3DGS & Compile CUDA Rasterizer\n",
        "import os, sys\n",
        "print(\"\ud83d\ude80 Setting up 3D Gaussian Splatting environment on GPU...\")\n",
        "\n",
        "# 1. Clone repository\n",
        "if not os.path.exists(\"/content/gaussian-splatting\"):\n",
        "    !git clone --recursive https://github.com/camenduru/gaussian-splatting /content/gaussian-splatting\n",
        "\n",
        "%cd /content/gaussian-splatting\n",
        "\n",
        "# 2. Pin setuptools<82 to match PyTorch 2.x and install ninja for fast CUDA compilation\n",
        "!pip install --upgrade \"setuptools<82\" wheel ninja packaging plyfile tqdm\n",
        "\n",
        "# 3. Compile CUDA rasterizer with --no-build-isolation\n",
        "print(\"\u26a1 Compiling diff-gaussian-rasterization CUDA extension (this takes ~60-90s, please don't cancel!)...\")\n",
        "!pip install --no-build-isolation ./submodules/diff-gaussian-rasterization\n",
        "\n",
        "print(\"\u26a1 Compiling simple-knn CUDA extension...\")\n",
        "!pip install --no-build-isolation ./submodules/simple-knn\n",
        "\n",
        "print(\"\n\u2705 Step 1 Complete: CUDA Rasterizer successfully compiled!\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": [
        "# @title Step 2: Upload Your photogrammetry_frames.zip File\n",
        "from google.colab import files\n",
        "import os, shutil, glob\n",
        "\n",
        "print(\"\ud83d\udce4 Click 'Choose Files' below to upload the photogrammetry_frames.zip exported from OralRevolution Studio:\")\n",
        "uploaded = files.upload()\n",
        "\n",
        "dataset_dir = \"/content/my_dataset\"\n",
        "input_dir = os.path.join(dataset_dir, \"input\")\n",
        "os.makedirs(input_dir, exist_ok=True)\n",
        "\n",
        "for fname in uploaded.keys():\n",
        "    if fname.endswith(\".zip\"):\n",
        "        print(f\"\ud83d\udce6 Unzipping {fname}...\")\n",
        "        !unzip -q \"{fname}\" -d /content/temp_extract/\n",
        "        extracted_imgs = glob.glob(\"/content/temp_extract/**/*.jpg\", recursive=True) + glob.glob(\"/content/temp_extract/**/*.png\", recursive=True)\n",
        "        for img in extracted_imgs:\n",
        "            shutil.copy(img, input_dir)\n",
        "        print(f\"\u2705 Successfully imported {len(extracted_imgs)} frames into dataset!\")\n",
        "        break\n",
        "    else:\n",
        "        print(\"\u26a0\ufe0f Please upload the .zip file containing the frames.\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": [
        "# @title Step 3: Run COLMAP Camera Tracking & Train 3D Gaussian Splats\n",
        "%cd /content/gaussian-splatting\n",
        "\n",
        "# 1. Run COLMAP feature extraction & SfM\n",
        "print(\"\ud83d\udd0d 1/2: Computing 3D Camera Poses with COLMAP SfM...\")\n",
        "!python convert.py -s /content/my_dataset\n",
        "\n",
        "# 2. Train 3D Gaussian Splats (7,000 steps gives crisp photorealism in ~4 minutes)\n",
        "print(\"\n\ud83d\udd25 2/2: Training 3D Gaussian Splatting Neural Ellipsoids...\")\n",
        "!python train.py -s /content/my_dataset -m /content/output_model --iterations 7000\n",
        "print(\"\n\ud83c\udf89 Training Complete! Model is ready for download.\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": [
        "# @title Step 4: Download Your Trained 3D Model (.PLY)\n",
        "from google.colab import files\n",
        "import os, glob\n",
        "\n",
        "ply_path = \"/content/output_model/point_cloud/iteration_7000/point_cloud.ply\"\n",
        "if os.path.exists(ply_path):\n",
        "    print(f\"\u2b07\ufe0f Downloading {ply_path} ({os.path.getsize(ply_path) / (1024*1024):.1f} MB)...\")\n",
        "    files.download(ply_path)\n",
        "    print(\"\n\u2728 Done! Once downloaded, drag and drop 'point_cloud.ply' into your OralRevolution Studio to view in 3D!\")\n",
        "else:\n",
        "    all_ply = glob.glob(\"/content/output_model/**/*.ply\", recursive=True)\n",
        "    if all_ply:\n",
        "        files.download(all_ply[-1])\n",
        "    else:\n",
        "        print(\"\u274c Could not find output point_cloud.ply file. Check Step 3 output.\")"
      ]
    }
  ]
}