deform_tools/examples/mesh_guided_deformation_ARAP.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deformation with Extracted Mesh\n",
    "\n",
    "We use Trex as the example to show how to use ARAP for mesh-guided deformation.\n",
    "\n",
    "We will use Open3D to perform ARAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import open3d as o3d\n",
    "import numpy as np\n",
    "\n",
    "sys.path.append('../')\n",
    "from pcd_editing import write_pth, get_pcd\n",
    "from mesh_editing import mesh_fix\n",
    "from mesh2pcd import mesh2pcd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Prepare Point Cloud and Mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_dir = '../../checkpoints/nerfsynth_sdf/trex/'\n",
    "pth_file = save_dir + '160000_net_ray_marching.pth'\n",
    "get_pcd(pth_file, save_dir + '160000_pcd.ply')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cd ../../run; python test_ft.py --config ../dev_scripts/spidr/trex.ini --run_mode=sdf --marching_cube"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ARAP requires the mesh to be a single connected component. We might need to fix the mesh.\n",
    "\n",
    "`pymeshfix` provides a simple meshfix function to merge disconnected components and close holes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_fix(save_dir + '160000_mesh.obj') # --> 160000_mesh_fix.obj"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Deform Mesh with ARAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh = o3d.io.read_triangle_mesh(save_dir + '160000_mesh_fix.obj')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `draw_geometries_with_vertex_selection` to select the vertices to be deformed.\n",
    "\n",
    "No worry, in this example, we will provide the vertex selection for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Original Mesh')\n",
    "# R = mesh.get_rotation_matrix_from_xyz((0, np.pi, 0))\n",
    "mesh.compute_vertex_normals()\n",
    "o3d.visualization.draw_geometries_with_vertex_selection([mesh])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# leg + base\n",
    "static_pnt = np.array([\n",
    "    [-0.92, 0.02, -0.31],\n",
    "    [0.92, 0.01, -0.31],\n",
    "    [0.47, 0.80, -0.31],\n",
    "    [-0.48, -0.79, -0.31],\n",
    "    [0.80, -0.47, -0.31],\n",
    "    [-0.45, 0.80, -0.31],\n",
    "    [-0.01, 0.93, -0.32],\n",
    "    [-0.20, 0.03, 0.62],\n",
    "    [0.22, 0.04, 0.62],\n",
    "    [-0.25, 0.05, -0.04],\n",
    "    [0.17, 0.05, -0.07],\n",
    "])\n",
    "# tail\n",
    "tail_pnt = np.array([\n",
    "    [-0.00, 1.58, 0.37],\n",
    "    [-0.02, 1.27, 0.28],\n",
    "    [-0.02, 0.90, 0.30],\n",
    "])\n",
    "# head\n",
    "head_pnt = np.array([\n",
    "    [-0.00, -1.41, 0.90],\n",
    "    [-0.00, -1.03, 1.06],\n",
    "    [0.12, -1.28, 0.82],\n",
    "    [-0.13, -1.28, 0.82],\n",
    "    [0.10, -1.11, 0.66],\n",
    "    [-0.11, -1.07, 0.67],\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices = np.asarray(mesh.vertices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_closest_pnt_idx(pnt, vertices):\n",
    "    dist = np.linalg.norm(vertices[..., None, :] - pnt[None, ...], axis=-1)\n",
    "    return np.argmin(dist, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "static_ids = get_closest_pnt_idx(static_pnt, vertices)\n",
    "tail_ids = get_closest_pnt_idx(tail_pnt, vertices)\n",
    "head_ids = get_closest_pnt_idx(head_pnt, vertices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "static_pos = np.array([vertices[id] for id in static_ids])\n",
    "# define deformation\n",
    "rot_center = np.array([0, -0.02, 0.58])\n",
    "\n",
    "tail_pos = np.array([vertices[id] for id in tail_ids])\n",
    "R = o3d.geometry.get_rotation_matrix_from_xyz(np.array([-28/180*np.pi, 0, 10/180*np.pi]))\n",
    "tail_pos_r = (R @ (tail_pos - rot_center).T).T + rot_center\n",
    "\n",
    "head_pos = np.array([vertices[id] for id in head_ids])\n",
    "R = o3d.geometry.get_rotation_matrix_from_xyz(np.array([-15/180*np.pi, 0, 0]))\n",
    "head_pos_r = (R @ (head_pos - rot_center).T).T + rot_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Open3D DEBUG] [DeformAsRigidAsPossible] setting up S'\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] done setting up S'\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] setting up system matrix L\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] done setting up system matrix L\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] setting up sparse solver\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] done setting up sparse solver\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=0, energy=2.131675e-01\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=1, energy=5.102009e-02\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=2, energy=2.601864e-02\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=3, energy=1.679037e-02\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=4, energy=1.237554e-02\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=5, energy=9.787330e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=6, energy=8.080551e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=7, energy=6.891443e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=8, energy=6.010716e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=9, energy=5.330662e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=10, energy=4.789261e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=11, energy=4.347888e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=12, energy=3.981142e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=13, energy=3.671608e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=14, energy=3.406935e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=15, energy=3.178112e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=16, energy=2.978403e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=17, energy=2.802670e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=18, energy=2.646919e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=19, energy=2.508000e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=20, energy=2.383395e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=21, energy=2.271064e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=22, energy=2.169337e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=23, energy=2.076832e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=24, energy=1.992396e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=25, energy=1.915059e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=26, energy=1.843997e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=27, energy=1.778507e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=28, energy=1.717985e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=29, energy=1.661909e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=30, energy=1.609827e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=31, energy=1.561345e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=32, energy=1.516117e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=33, energy=1.473840e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=34, energy=1.434247e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=35, energy=1.397102e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=36, energy=1.362195e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=37, energy=1.329338e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=38, energy=1.298366e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=39, energy=1.269130e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=40, energy=1.241493e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=41, energy=1.215337e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=42, energy=1.190551e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=43, energy=1.167037e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=44, energy=1.144705e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=45, energy=1.123474e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=46, energy=1.103267e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=47, energy=1.084019e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=48, energy=1.065665e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=49, energy=1.048150e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=50, energy=1.031420e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=51, energy=1.015427e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=52, energy=1.000126e-03\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=53, energy=9.854768e-04\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=54, energy=9.714401e-04\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=55, energy=9.579810e-04\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=56, energy=9.450667e-04\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=57, energy=9.326667e-04\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=58, energy=9.207526e-04\n",
      "[Open3D DEBUG] [DeformAsRigidAsPossible] iter=59, energy=9.092981e-04\n"
     ]
    }
   ],
   "source": [
    "# ARAP\n",
    "constraint_ids = o3d.utility.IntVector(np.concatenate([static_ids, tail_ids, head_ids], 0).tolist())\n",
    "constraint_pos = o3d.utility.Vector3dVector(np.concatenate([static_pos, tail_pos_r, head_pos_r], 0))\n",
    "\n",
    "with o3d.utility.VerbosityContextManager(\n",
    "        o3d.utility.VerbosityLevel.Debug) as cm:\n",
    "            mesh_prime = mesh.deform_as_rigid_as_possible(\n",
    "                constraint_ids, constraint_pos,\n",
    "                #   energy=o3d.geometry.DeformAsRigidAsPossibleEnergy.Smoothed,\n",
    "                max_iter=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deformed Mesh\n"
     ]
    }
   ],
   "source": [
    "os.environ['DISPLAY'] = ':0'\n",
    "print('Deformed Mesh')\n",
    "mesh_prime.compute_vertex_normals()\n",
    "o3d.visualization.draw_geometries([mesh_prime, mesh])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note to make sure the two meshes have the same vertex order. We write both meshes with open3d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;33m[Open3D WARNING] Write OBJ can not include triangle normals.\u001b[0;m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save\n",
    "o3d.io.write_triangle_mesh(save_dir + '160000_mesh_fix_arap.obj', mesh_prime)\n",
    "o3d.io.write_triangle_mesh(save_dir + '160000_mesh_fix_o3d.obj', mesh)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Transfer Deformation to Point Cloud & Ckpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read triangle mesh: V=188490, F=377156\n",
      "read pcd: V=170454\n"
     ]
    }
   ],
   "source": [
    "mesh2pcd(save_dir + '160000_mesh_fix_o3d.obj', save_dir + '160000_mesh_fix_arap.obj', save_dir + '160000_pcd.ply', save_dir + '160000_pcd_deform.ply')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pth_new_file = save_dir + '160010_net_ray_marching.pth'\n",
    "write_pth(pth_file, pth_new_file, save_dir + '160000_pcd_deform.ply')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Render Deformed Scene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cd ../../run; python test_ft.py --config ../dev_scripts/spidr/trex.ini --run_mode=sdf --split=test --resume_iter 160010 --test_end_id 5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The illumination updates require to re-bake the depth maps from the light sources (since the object geometry has changed):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cd ../../run; python test_ft.py --config ../dev_scripts/spidr/trex.ini --run_mode=sdf --bake_light --down_sample=0.5 --resume_iter=160010 --SR=60"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can render the deformed scene with illumination updates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cd ../../run; python test_ft.py --config ../dev_scripts/spidr/trex.ini --run_mode=lighting --split=test --resume_iter 160010 --test_end_id 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PBR')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "non_pbr = '../../checkpoints/nerfsynth_sdf/trex/test_160010/images/step-0002-coarse_raycolor.png'\n",
    "pbr = '../../checkpoints/nerfsynth_sdf/trex/test_160010/images/step-0002-brdf_combine_raycolor.png'\n",
    "\n",
    "non_pbr = plt.imread(non_pbr)\n",
    "pbr = plt.imread(pbr)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(non_pbr)\n",
    "plt.title('Non-PBR')\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(pbr)\n",
    "plt.title('PBR')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch1.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}