tutorial-pointcloud-processingpipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial: point cloud processing using Open3d\n",
    "\n",
    "In this tutorial, we will see how to process point clouds to produce watertight meshes or to analyse the geometry of the acquired scene, using Python.\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "To this end, we will use the following tools:\n",
    " - Dataset: https://3dard.cnrs.fr. Data will be fetched using the python front-end https://github.com/3D-ARD/python-module\n",
    " - Processing library: http://www.open3d.org/. Checkout the documentation at http://www.open3d.org/docs/latest \n",
    " \n",
    "Read the documentation of each project to know how to install and use it.\n",
    "\n",
    "## Perequisite\n",
    "\n",
    "In this first section, we demonstrate how to load point clouds from 3dard, and visualize them using open3d.\n",
    "\n",
    "See this as a sandbox, and feel free to experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "from ThreeDARD import *\n",
    "import open3d as o3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Link to local file system\n",
    "if not dataset.isInitialized(): \n",
    "    dataset.init(\"./dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Retrieve assets.json from cache\n",
      "['Bone', 'Charango', 'Great_tit_skull', 'Haut_Carre', 'Junk_ship', 'Lady_of_Elche', 'Maitreya_Buddha']\n"
     ]
    }
   ],
   "source": [
    "# List all assets availables\n",
    "d = dataset.getDatasetMeta()\n",
    "print (d[\"assets\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Retrieve Maitreya_Buddha/0006/unit.json from cache\n",
      "{'name': 'Scans', 'slug': 'scans', 'author': 'MARCADET Quentin <quentin.marcadet@gmail.com>', 'type': 'acquired', 'id': '0006', 'hardware': ['HandyScan Creaform'], 'software': ['Geomagic Wrap'], 'description': 'Set of 3 unaligned scans of Maitreya Buddha Statue in .ply format file with their text files containing point coordinates', 'details': '', 'inputs': [], 'related': [], 'outputs': [{'filename': 'scan01.ply'}, {'filename': 'scan02.ply'}, {'filename': 'scan03.ply'}]}\n"
     ]
    }
   ],
   "source": [
    "# Get unit metadata, download point cloud and display\n",
    "unit=dataset.getUnitMeta(\"Maitreya_Buddha\", \"0006\")\n",
    "print(unit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that load a file and return it as an open3d point cloud\n",
    "# param path string indicating where the file is located. Can be retrieved using getUnitDataPath\n",
    "# param color array indicating the color to be applied on the point cloud for display, eg [1, 0.706, 0]\n",
    "def loadPointCloud(path, color=None):\n",
    "    m = o3d.io.read_point_cloud(plypath)\n",
    "    if not color is None:\n",
    "        m.paint_uniform_color(color)\n",
    "    return m\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Retrieve Maitreya_Buddha/0006/scan01.ply from cache\n",
      "    Retrieve Maitreya_Buddha/0006/scan02.ply from cache\n",
      "    Retrieve Maitreya_Buddha/0006/scan03.ply from cache\n",
      "[PointCloud with 2403956 points., PointCloud with 2964755 points., PointCloud with 2346858 points.]\n"
     ]
    }
   ],
   "source": [
    "# Load all scans\n",
    "scans = []\n",
    "for unitData in unit['outputs']:\n",
    "    plypath = dataset.getUnitDataPath(\"Maitreya_Buddha\", \"0006\", unitData['filename'])\n",
    "    scans.append( loadPointCloud(plypath,np.random.rand(3)))\n",
    "print (scans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display point clouds\n",
    "o3d.visualization.draw_geometries(scans)"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should get a window with something like this:\n",
    "\n",
    "![image.png](attachment:1010a417-a363-4c8f-97b2-322472898706.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment 1: Reconstruct watertight mesh\n",
    "Starting from the serie of point clouds of the Maitreya_Buddha, reconstruct a unique mesh representing the entire object.\n",
    "\n",
    "A pipeline would be:\n",
    " - Compute normals\n",
    " - Refine registration\n",
    " - Reconstruct mesh\n",
    "\n",
    "Outcomes: generate a zip file containing your python script (or a jupyter notebook) *and* the generated data. Your code should be documented: explain how the pipeline works, what choices have you made, and what are the outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment 2: Indoor scene analysis with massive point clouds\n",
    "We now focus on the Haute Carré asset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Retrieve Haut_Carre/asset.json from cache\n",
      "{'name': 'Haut Carre', 'slug': 'haut_carre', 'uuid': '0000', 'description': 'Annex building of the Haut Carre, Bordeaux University', 'owner': 'Bordeaux University, Talence (France, Gironde, 33)', 'url': 'https://www.u-bordeaux.fr/Universite/L-universite-de-Bordeaux/Histoire-et-patrimoine/Talence/L-Agora-du-Haut-Carre', 'units': ['0000', '0001', '0002', '0003', '0004', '0005', '0006', '0007', '0008', '0009', '0010', '0011', '0012', '0013']}\n"
     ]
    }
   ],
   "source": [
    "# Get asset metadata\n",
    "assetName = \"Haut_Carre\"\n",
    "asset=dataset.getAssetMeta(assetName)\n",
    "print(asset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Retrieve Haut_Carre/0000/unit.json from cache\n",
      "Raw Pictures Exterior: Set of JPG and NEF pictures. Exterior of the annexe building\n",
      "    Retrieve Haut_Carre/0001/unit.json from cache\n",
      "Raw Pictures Exterior Drone: Set of JPG, DNG and xmp pictures. Exterior with drone of the annexe building\n",
      "    Retrieve Haut_Carre/0002/unit.json from cache\n",
      "Raw Pictures Interior: Set of JPG and NEF pictures. Interior of the annexe building\n",
      "    Retrieve Haut_Carre/0003/unit.json from cache\n",
      "Modified Pictures Exterior: Set of JPG pictures, modified to adjust black and white level. Exterior ofthe annex building\n",
      "    Retrieve Haut_Carre/0004/unit.json from cache\n",
      "Modified Pictures Interior: Set of JPG pictures, modified to adjust black and white level. Interior of the annex building\n",
      "    Retrieve Haut_Carre/0005/unit.json from cache\n",
      "Scans Totality Annex Building E57: Set of .e57 of the totality of the annex building. Exterior and interior\n",
      "    Retrieve Haut_Carre/0006/unit.json from cache\n",
      "Scans Totality Frame E57: Set of .e57 of the totality of the wood frame.\n",
      "    Retrieve Haut_Carre/0007/unit.json from cache\n",
      "Scans Totality Annex Building PTX: Set of .ptx of the totality of the annex building. Exterior and interior\n",
      "    Retrieve Haut_Carre/0008/unit.json from cache\n",
      "Scans Totality Frame E57: Set of .ptx of the totality of the wood frame.\n",
      "    Retrieve Haut_Carre/0009/unit.json from cache\n",
      "Dense Point Cloud Top: Dense point cloud of the top of the annex building in .pcd format file. Total points : 469 million. \n",
      "    Retrieve Haut_Carre/0010/unit.json from cache\n",
      "Dense Point Cloud Bottom: Dense point cloud of the bottom of the annex building in .pcd format file. Total points : 94 million. \n",
      "    Retrieve Haut_Carre/0011/unit.json from cache\n",
      "Dense Point Cloud Total: Dense point cloud of the totality of the annex building in .pcd format file. Created with the merge of the top and bottom dense cloud. Cleaned version. Total points : 368,5 million. \n",
      "    Retrieve Haut_Carre/0012/unit.json from cache\n",
      "Mesh: Mesh of the annex building in .obj format file. Created with the depth map mode. Cleaned version. Total tris : 126,1 million. \n",
      "    Retrieve Haut_Carre/0013/unit.json from cache\n",
      "Mesh_lod: Mesh in different level of details of the annex building, old building. File in .obj format.\n"
     ]
    }
   ],
   "source": [
    "# List details about the available units\n",
    "for uname in asset['units']:\n",
    "    unit=dataset.getUnitMeta(assetName, uname)\n",
    "    print (unit['name'] + \": \" + unit['description'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_________________\n",
    "As you can see, there are very large point clouds, made of hunders of thousands of points.\n",
    "\n",
    "**Objectives:**\n",
    "\n",
    "The goal of this exercise is to define a pipeline allowing to load, align, visualize, and analyse the geometry of the scene (e.g. primitive detection).\n",
    "In order the handle the size of the data, you might need to sample the input point clouds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here you go !"
   ]
  }
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