diff --git a/.gitignore b/.gitignore
index 719bc76..bb3c1a6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,4 @@
 /docs/static/items
 /ipython/.ipynb_checkpoints
 .DS_Store
+*.bz2
diff --git a/ipython/bundle_adjustment.ipynb b/ipython/bundle_adjustment.ipynb
index 4f84347..a7c0914 100644
--- a/ipython/bundle_adjustment.ipynb
+++ b/ipython/bundle_adjustment.ipynb
@@ -15,7 +15,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A bundle adjusmtent problem arises in 3-D reconstruction and it can be formulated as follows (taken from https://en.wikipedia.org/wiki/Bundle_adjustment):\n",
+    "A bundle adjusmtent problem arises in 3D reconstruction and it can be formulated as follows (taken from https://en.wikipedia.org/wiki/Bundle_adjustment):\n",
     "\n",
     "> Given a set of images depicting a number of 3D points from different viewpoints, bundle adjustment can be defined as the problem of simultaneously refining the 3D coordinates describing the scene geometry as well as the parameters of the relative motion and the optical characteristics of the camera(s) employed to acquire the images, according to an optimality criterion involving the corresponding image projections of all points."
    ]
@@ -24,14 +24,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "More precisely. We have a set of points in real world defined by their coordinates $(X, Y, Z)$ in some apriori chosen \"world coordinate frame\". We photograph these points by different cameras, which are characterized by their orientation and translation relative to the world coordinate frame and also by focal length and two radial distortion parameters (9 parameters in total). Then we precicely measure 2-D coordinates $(x, y)$ of the points projected by the cameras on images. Our task is to refine 3-D coordinates of original points as well as camera parameters, by minimizing the sum of squares of reprojecting errors."
+    "More specifically. \n",
+    "We are given a set of points in real world defined by their coordinates $(X, Y, Z)$ in an apriori chosen world coordinate frame. \n",
+    "These points are photographed by different cameras, which are characterized by their orientation and translation relative to the world coordinate frame and also by focal length and two radial distortion parameters (9 parameters in total). \n",
+    "The corresponding 2D images coordinates $(x, y)$ of 3D points are measured.\n",
+    "The task is to refine 3D coordinates of the points as well as the camera parameters by minimizing the sum of squares of reprojecting errors, given an initial guess for all unknowns."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let $\\pmb{P} = (X, Y, Z)^T$ - a radius-vector of a point, $\\pmb{R}$ - a rotation matrix of a camera, $\\pmb{t}$ - a translation vector of a camera, $f$ - its focal distance, $k_1, k_2$ - its distortion parameters. Then the reprojecting is done as follows:\n",
+    "Let $\\pmb{P} = (X, Y, Z)^T$ - a radius-vector of a point, $\\pmb{R}$ - a rotation matrix of a camera, $\\pmb{t}$ - a translation vector of a camera, $f$ - its focal length in pixels, $k_1, k_2$ - its radial distortion parameters. Then the transformation by a camera is defined as follows:\n",
     "\n",
     "\\begin{align}\n",
     "\\pmb{Q} = \\pmb{R} \\pmb{P} + \\pmb{t} \\\\\n",
@@ -44,7 +48,39 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The resulting vector $\\pmb{p}=(x, y)^T$ contains image coordinates of the original point. This model is called \"pinhole camera model\", a very good notes about this subject I found here http://www.comp.nus.edu.sg/~cs4243/lecture/camera.pdf"
+    "The resulting vector $\\pmb{p}=(x, y)^T$ contains image coordinates of the original point.\n",
+    "This model is called \"pinhole camera model\", you can read more about it here http://www.comp.nus.edu.sg/~cs4243/lecture/camera.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additional notes regarding the formulas above:\n",
+    "    \n",
+    "1. $\\pmb{R}$ is the rotation matrix which projects from the world axes to the camera axes.\n",
+    "2. $\\pmb{t}$ is the translation vector from the camera origin to the world origin expressed in the camera axes.\n",
+    "3. The minus sign for $\\pmb{q}$ accounts for a particular camera axes convention: $x$ points right, $y$ points up and $z$ points backwards such that observed points have negative $z$ values. Usually $y$ and $z$ directions are chosen to be the opposite, but here we stick with the convention used in BAL dataset (see next).\n",
+    "4. The components of $\\pmb{p}$ are pixel coordinates with $(0, 0)^T$ being the image center. Usually the pixel coordinates origin is chosen to be at the top left corner of an image and the image center parameters $c_x, c_y$ are considered or estimated. But here we stick with the convention used in BAL dataset where center parameters are not explicitly modeled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Camera position $\\pmb{t}'$ and rotation $\\pmb{R}'$ relative to the world frame can be computed from $\\pmb{R}, \\pmb{t}$ as follows:\n",
+    "\n",
+    "\\begin{align}\n",
+    "\\pmb{t}' = -\\pmb{R}^T t \\\\\n",
+    "\\pmb{R}' = \\pmb{R}^T\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The cost function to be minimized is the sum of squares of the reprojection errors: $F = \\frac{1}{2} \\sum_{i=1}^N \\lVert \\pmb{p}_i - \\tilde{\\pmb{p}}_i \\rVert^2$. Where $N$ is the total number of 2D observations, $\\pmb{p}_i$ is 2D coordinates of a point computed by the projection formulas from the estimated parameters, and $\\pmb{\\tilde{p}}_i$ is observed 2D coordinates."
    ]
   },
   {
@@ -58,15 +94,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now let's start solving some real bundle adjusment problem. We'll take a problem from http://grail.cs.washington.edu/projects/bal/."
+    "Now let's solve a real bundle adjusment problem from BAL dataset: http://grail.cs.washington.edu/projects/bal/."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from __future__ import print_function"
@@ -75,9 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import urllib\n",
@@ -96,9 +128,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "BASE_URL = \"http://grail.cs.washington.edu/projects/bal/data/ladybug/\"\n",
@@ -109,9 +139,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "if not os.path.isfile(FILE_NAME):\n",
@@ -128,9 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def read_bal_data(file_name):\n",
@@ -164,9 +190,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "camera_params, points_3d, camera_indices, point_indices, points_2d = read_bal_data(FILE_NAME)"
@@ -178,21 +202,24 @@
    "source": [
     "Here we have numpy arrays: \n",
     "\n",
-    "1. `camera_params` with shape `(n_cameras, 9)` contains initial estimates of parameters for all cameras. First 3 components in each row form a rotation vector (https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula), next 3 components form a translation vector, then a focal distance and two distortion parameters.\n",
+    "1. `camera_params` with shape `(n_cameras, 9)` contains initial estimates of parameters for all cameras. First 3 components in each row form a rotation vector (https://en.wikipedia.org/wiki/Rodrigues%27_rotation_formula) corresponding to $\\pmb{R}$, next 3 components form a translation vector $\\pmb{t}$, then a focal distance and two distortion parameters.\n",
     "2. `points_3d` with shape `(n_points, 3)` contains initial estimates of point coordinates in the world frame.\n",
-    "3. `camera_ind` with shape `(n_observations,)` contains indices of cameras (from 0 to `n_cameras - 1`) involved in each observation.\n",
-    "4. `point_ind` with shape `(n_observations,)` contatins indices of points (from 0 to `n_points - 1`) involved in each observation.\n",
+    "3. `camera_indices` with shape `(n_observations,)` contains indices of cameras (from 0 to `n_cameras - 1`) involved in each observation.\n",
+    "4. `point_indices` with shape `(n_observations,)` contatins indices of points (from 0 to `n_points - 1`) involved in each observation.\n",
     "5. `points_2d` with shape `(n_observations, 2)` contains measured 2-D coordinates of points projected on images in each observations.\n",
     "\n",
-    "And the numbers are:"
+    "In this dataset each image is taken from a different camera meaning that each camera has its own rotation, translation and optical parameters. \n",
+    "Another typical scenario is when one camera is used to take images from different vantages, in this case we would estimate many rotation and translation parameters and one set of optical parameters.\n",
+    "\n",
+    "So in this dataset there is a one-to-one correspondence between cameras and images, such that you can think of \"camera index\" as of \"image index\".\n",
+    "\n",
+    "Let's output the problem's dimenstions:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -229,46 +256,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now define the function which returns a vector of residuals. We use numpy vectorized computations:"
+    "Now define the function which returns a vector of residuals. \n",
+    "To create a rotation transform we use `from_rotvec` method of `Rotation` class availble in `scipy.spatial.transform`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "def rotate(points, rot_vecs):\n",
-    "    \"\"\"Rotate points by given rotation vectors.\n",
-    "    \n",
-    "    Rodrigues' rotation formula is used.\n",
-    "    \"\"\"\n",
-    "    theta = np.linalg.norm(rot_vecs, axis=1)[:, np.newaxis]\n",
-    "    with np.errstate(invalid='ignore'):\n",
-    "        v = rot_vecs / theta\n",
-    "        v = np.nan_to_num(v)\n",
-    "    dot = np.sum(points * v, axis=1)[:, np.newaxis]\n",
-    "    cos_theta = np.cos(theta)\n",
-    "    sin_theta = np.sin(theta)\n",
-    "\n",
-    "    return cos_theta * points + sin_theta * np.cross(v, points) + dot * (1 - cos_theta) * v"
+    "from scipy.spatial.transform import Rotation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def project(points, camera_params):\n",
     "    \"\"\"Convert 3-D points to 2-D by projecting onto images.\"\"\"\n",
-    "    points_proj = rotate(points, camera_params[:, :3])\n",
-    "    points_proj += camera_params[:, 3:6]\n",
-    "    points_proj = -points_proj[:, :2] / points_proj[:, 2, np.newaxis]\n",
+    "    rotation = Rotation.from_rotvec(camera_params[:, :3])\n",
+    "    points_camera = rotation.apply(points) + camera_params[:, 3:6]\n",
+    "    points_proj = -points_camera[:, :2] / points_camera[:, 2, np.newaxis]\n",
     "    f = camera_params[:, 6]\n",
     "    k1 = camera_params[:, 7]\n",
     "    k2 = camera_params[:, 8]\n",
@@ -281,9 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def fun(params, n_cameras, n_points, camera_indices, point_indices, points_2d):\n",
@@ -307,9 +316,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from scipy.sparse import lil_matrix"
@@ -318,9 +325,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices):\n",
@@ -344,92 +349,48 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we are ready to run optimization. Let's visualize residuals evaluated with the initial parameters."
+    "Now we are ready to run optimization. \n",
+    "We stack the camera parameters and the 3D point coordinates in a 1-dimensional vector, because it is expected by `least_squares` function.,"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x0 = np.hstack((camera_params.ravel(), points_3d.ravel()))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "f0 = fun(x0, n_cameras, n_points, camera_indices, point_indices, points_2d)"
+    "Compute sparsity structure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1067696d8>]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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1LeefD+y+e/X3wIEt847j9bTxxuHPmT49enm1QqEVQxyt/OMfu/sxmx9EA/PD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\nIRaoGAghhFigYiCEEGLh/wHjTnp/SG4rlgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105203cc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "plt.plot(f0)"
+    "A = bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "A = bundle_adjustment_sparsity(n_cameras, n_points, camera_indices, point_indices)"
+    "And finally run the optimization with `least_squares` function. \n",
+    "\n",
+    "We set `scaling='jac'` to automatically scale the variables and equalize their influence on the cost function. This is necessary because we have 5 kind of parameters (rotation, translation, focal length, distortion and 3D coordinates) of completely different nature."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import time\n",
@@ -438,10 +399,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -454,15 +413,15 @@
       "       3              5         1.4163e+04      1.91e+03       2.86e+02       1.21e+05    \n",
       "       4              7         1.3695e+04      4.67e+02       1.32e+02       2.51e+04    \n",
       "       5              8         1.3481e+04      2.14e+02       2.24e+02       1.54e+04    \n",
-      "       6              9         1.3436e+04      4.55e+01       3.18e+02       2.73e+04    \n",
-      "       7             10         1.3422e+04      1.37e+01       6.84e+01       2.20e+03    \n",
-      "       8             11         1.3418e+04      3.70e+00       1.28e+02       7.88e+03    \n",
-      "       9             12         1.3414e+04      4.19e+00       2.64e+01       6.24e+02    \n",
-      "      10             13         1.3412e+04      1.88e+00       7.55e+01       2.61e+03    \n",
-      "      11             14         1.3410e+04      2.09e+00       1.77e+01       4.97e+02    \n",
-      "      12             15         1.3409e+04      1.04e+00       4.02e+01       1.32e+03    \n",
+      "       6              9         1.3436e+04      4.54e+01       3.18e+02       2.73e+04    \n",
+      "       7             10         1.3422e+04      1.38e+01       6.79e+01       2.19e+03    \n",
+      "       8             11         1.3418e+04      3.72e+00       1.31e+02       8.07e+03    \n",
+      "       9             12         1.3414e+04      4.30e+00       2.62e+01       6.11e+02    \n",
+      "      10             13         1.3412e+04      1.89e+00       7.68e+01       2.66e+03    \n",
+      "      11             14         1.3410e+04      2.12e+00       1.76e+01       5.06e+02    \n",
+      "      12             15         1.3409e+04      1.02e+00       3.98e+01       1.30e+03    \n",
       "`ftol` termination condition is satisfied.\n",
-      "Function evaluations 15, initial cost 8.5091e+05, final cost 1.3409e+04, first-order optimality 1.32e+03.\n"
+      "Function evaluations 15, initial cost 8.5091e+05, final cost 1.3409e+04, first-order optimality 1.30e+03.\n"
      ]
     }
    ],
@@ -475,16 +434,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Optimization took 33 seconds\n"
+      "Optimization took 27 seconds\n"
      ]
     }
    ],
@@ -496,38 +453,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Setting `scaling='jac'` was done to automatically scale the variables and equalize their influence on the cost function (clearly the camera parameters and coordinates of the points are very different entities). This option turned out to be crucial for successfull bundle adjustment."
+    "To assess the optimization efficiency let's plot the reprojection errors (residual values) before and after the optimization."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Now let's plot residuals at the found solution:"
+    "fun0 = fun(x0, n_cameras, n_points, camera_indices, point_indices, points_2d)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10de73438>]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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QqZM5c7QPIv0kddBTSzL3FRS7BP3CC92tP8aPN8/nn+8uV/c2AYz7tHKPPUyR\nybJlwZ2A7bJL/uwDMElv1ari7dyDjtLXrAmep1cvd0uLcv/ogwf7n6EFCbseZ5NBwL9tudM11/hf\nexK0bG9dRBg//Wm4PnD82EVlxXz9dbjp4lBq237llXzjhjiMHOk/ftSo4KvrTz452vddjWKgYsV8\nfusv56rpWpaZxODcid5+u9lB2G6+2X+H8tvfmue2bcNdGAPEe6TbrRtw9dXhp+/cufgfJKiytmPH\n8Ecx1TqSD3vthfN369GjMFF4XXBB+KvVBw40vXTGqdRO6aCDgJUrwy+v0qu/Syl1xrD//vFuE3fe\nGX2eqOtv2dLd/5Cz2W5cHQ5GTT79+5d3EFKrMpMYnJW5o0a5+275xS/cxTxZNXt2vqy/HN4K13KU\nuxOIMt8LL4Rr5WOzmxd6K4nj5r2WoJhKLiqMUsdx8MGly7SD2AcRxc6gaqGSNeo2KeLuZ+ull8zz\nDTf4FyXHrRa+06RlJjHEWa7n98M6L/aK84ffYYd85e5ee5Xud6cY+0roYkqdGVWjfPTAA6M197Pr\na5K8uGjBAv+uK4L861+FF/sBpq6h3MptPy1bmjLtcjz5pEmmYRssVEu/fsVb93lVesZibzelrtqn\n+GQmMcR5yu39szz6aHL9vrdpk+9YrVLFemocNMi0oy/VsV0WO/WqRkw77xxtPTvu6N8H09FHRysq\nStI225iWVMV2/pVeLR7GW2+5Lxp79NFoZ3+V/P7ldC4YRrHvlGcMGWqVFKUYICpneWXcZwxxCmrT\nD4S/AjyLiYEqk/ZOrE8f9/8zajFcudvkZpsVdtBI1ZGJxPCTn4TvnREwV/mm/WfJGpHSfcenKUw/\nReQv7TOGSpWbGFavdpckRO0Aj8qXWGIQkXEARgH4xBr1a1Wd4TdtqStkvUqVoxf7I4nU9p3FALOT\n9etWOcvtr+++O+0IateBBxZezW8bPbp0H1CAqcgu1ilfkspNDM4WfEuXBncgWQ4eWBaX9BnD71X1\n96UmcvYMGYdSP+zVVxfeRauWxL2TPfro8rsYp+QddJDp8NHP0KHhbsK0Zk1l/QNdcYXZTsoRR/Gm\n9x4naWhOxbRJJ4ZQX2XQRTPlKnXGkOWyyz32CO6eOCnFrvKmpqHSFmG77eburDCKWtuh8owh+VZJ\n54nIHBG5Q0RYQhhCLVyvkbaJE1k0VUtqLTFQhWcMIjITgLPVtwBQAGMB3AzgClVVEbkKwO8B+Bbg\n1Dk6R8+VZADzAAAKf0lEQVTlcsiV2/DbUuqMgWrbHntE746d0jFjRukeX7OkWCebaauvr0e9faPr\nhFWUGFT1sNJTAQD+DCCwwKIu5rtm7L9/cL8/xa4VyIJq9bFDVA3F7vlB0XgPmsfbncUlILGiJBFx\n7oKHAki4Q4S8Sy4xNyzxUnXfYCWLRo8Ormgkitvzz5suToickqx8vk5E+gBoBLAEgM9dAcirZcvg\npolEcatG30NZxOaqxSWWGFSVlzQ1QWzWStT0ZeLKZ6oNPJKi5oDbeYY60SMiqhbu/ItjYiAicgiT\nNMLeMrdWMTEQUbPXuXPaEWQLEwMRNXt2P1JZ7pa/mpgYiKjZY48IbkwMRETkwsRARM0OL3ArjomB\niIhcmBiIqNk7zNEdKM8YmBiIiHDnndGmb+rJg4mBiJod1jEUx8RAREQuTAxEROTCxEBEzU6xvo5Y\nlMTEQETNUOfO0RNA9+7JxJJFTAxERA5BCePcc4H//Ke6saSFiYGImrWwPau2aAF06JBsLFnBxEBE\nzVrv3u7XrGOoMDGIyPEi8o6IbBCRvp73LheRhSIyX0QOryxMIiKqlkrv+TwXwHEAbnOOFJFeAH4O\noBeAbQE8IyI7qTIXExFlXUVnDKq6QFUXAvD2Zn4sgAdUtUFVlwBYCKBfJesiIqoGHr4mV8ewDYDl\njtcrrHFERJnSrVvaEWRPyaIkEZkJoItzFAAFMFZVp8cRRF1d3ffDuVwOuVwujsUSERX16adAu3bu\ncVk9Y6ivr0d9fX1V1iVxFPuLyPMALlLV2dbrywCoqk60Xs8AME5VX/OZl1UPRJQ6EZMkJk0Czjkn\nOEGIAF27AitXpptERASqmshNSeMsSnIGOA3AMBHZWES2A7AjgNdjXBcRUSJ4nFp5c9UhIrIcQH8A\nT4jIUwCgqvMATAUwD8CTAM7haQERUW2oqLmqqj4G4LGA964FcG0lyyciqjYewvLKZyIi8mBiICKy\nbLRR2hFkQ6VXPhMRNRlnngkcdRQwe3bakaSLZwxERJaNNwZ69gQmT047knQxMRARkQsTAxFRRE29\n5RITAxERuTAxEBGRCxMDERG5MDEQEZELEwMREbkwMRARkQsTAxERuTAxEBGRC/tKIiICcOONwJAh\naUeRDbHc2rOiAHhrTyKqISJAly7AqlW8tScRETUTTAxERORS6T2fjxeRd0Rkg4j0dYzvISJfichs\n63Fz5aESEVE1VFr5PBfAcQBu83lvkar29RlPREQZVlFiUNUFACAifhUgiVSKEBFRspKsY+hpFSM9\nLyIHJLgeIiKKUckzBhGZCaCLcxQABTBWVacHzPYRgO6q+rlV9/CYiPRW1S8rjpiIiBJVMjGo6mFR\nF6qq6wF8bg3PFpEPAOwMwPcW23V1dd8P53I55HK5qKskImrS6uvrUV9fX5V1xXKBm4g8D+BiVX3T\net0JwBpVbRSR7QG8AGB3Vf2Pz7y8wI2IagYvcCtBRIaIyHIA/QE8ISJPWW8dCOBtEZkNYCqA0X5J\ngYiIsoddYhARRcAzBiIianaYGIiIyIWJgYiIXJgYiIjIhYmBiIhcmBiIiCKYMgUYPTrtKJLFxEBE\nFMGIEUCnTmlHkSwmBiIicmFiICIiFyYGIiJyYWIgIoqoffu0I0gW+0oiIopowwZg0SJgl13SiyHJ\nvpKYGIiIahA70SMioqphYiAiIhcmBiIicmFiICIiFyYGIiJyYWIgIiKXihKDiFwnIvNFZI6IPCIi\nmzneu1xEFlrvH155qEREVA2VnjE8DWA3Ve0DYCGAywFARHoD+DmAXgCOBHCziCTS3jZt9fX1aYdQ\nEcafrlqOv5ZjB2o//iRVlBhU9RlVbbRe/hPAttbwYAAPqGqDqi6BSRr9KllXVtX6xsX401XL8ddy\n7EDtx5+kOOsYTgfwpDW8DYDljvdWWOOIiCjjWpWaQERmAujiHAVAAYxV1enWNGMBrFfV+xOJkoiI\nqqbivpJE5DQAowAMVNVvrXGXAVBVnWi9ngFgnKq+5jM/O0oiIipDJjvRE5FBAK4HcKCqfuYY3xvA\nvQD2gylCmglgJ/aWR0SUfSWLkkq4CcDGAGZajY7+qarnqOo8EZkKYB6A9QDOYVIgIqoNqXe7TURE\n2ZLqlc8iMkhE3hOR90Xk0hTjmCwiq0Tkbce4jiLytIgsEJG/i0gHx3u+F++JSF8Redv6PDc6xm8s\nIg9Y87wqIt1jjn9bEXlORN4VkbkiMqaWPoOItBaR10TkLSv+cbUUv7X8FiIyW0Sm1WDsS0TkX9b3\n/3oNxt9BRB6y4nlXRParlfhFZGfre59tPa8VkTGpx6+qqTxgktIiAD0AbARgDoBdU4rlAAB9ALzt\nGDcRwCXW8KUAJljDvQG8BVMM19P6DPaZ12sA9rWGnwRwhDX8CwA3W8MnwFzjEWf8XQH0sYbbA1gA\nYNca+wxtreeWMNfE9Kux+C8EcA+AaTW4/SwG0NEzrpbi/wuAkdZwKwAdail+x+doAeAjAN3Sjj/2\nDxfhS+gP4CnH68sAXJpiPD3gTgzvAehiDXcF8J5fnACegqlk7wpgnmP8MAC3WMMzAOxnDbcEsDrh\nz/IYgENr8TMAaAvgDQD71kr8MBd2zgSQQz4x1ETs1jL/DWBLz7iaiB/AZgA+8BlfE/F7Yj4cwD+y\nEH+aRUnei+A+RLYuguusqqsAQFVXAuhsjQ+6eG8bmM9gc36e7+dR1Q0A/iMiWyQRtIj0hDn7+SfM\nhlUTn8EqinkLwEoAM1V1Vg3FfwOAX8Fc32OrldhhxT1TRGaJyJk1Fv92AD4Vkbus4pjbRaRtDcXv\ndAKA+6zhVONn76rhxVlLn0zbY5H2AB4GcIGqfonCmDP7GVS1UVX3gjn67iciu6EG4heRowCsUtU5\nJZaZudgdBqhqXwA/AXCuiPwYNfDdW1oB6AvgT9ZnWAdzVF0r8ZsFimwE05XQQ9aoVONPMzGsAOCs\nBNnWGpcVq0SkCwCISFcAn1jjV8CUAdrsuIPGu+YRkZYANlPVNXEGKyKtYJLCFFV9vBY/AwCo6hcA\n6gEMqpH4BwAYLCKLAdwPYKCITAGwsgZiBwCo6sfW82qYYsh+qI3vHjBHxstV9Q3r9SMwiaJW4rcd\nCeBNVf3Uep1q/GkmhlkAdhSRHiKyMUyZ2LQU4xG4M+k0AKdZw6cCeNwxfphV078dgB0BvG6d7q0V\nkX4iIgBO8cxzqjX8MwDPJRD/nTBljH+otc8gIp3sVhcisgmAwwDMr4X4VfXXqtpdVbeH2YafU9WT\nAUzPeuwAICJtrTNNiEg7mHLuuaiB7x4ArOKW5SKyszXqEADv1kr8DifCHFjY0o0/iUqUCJUtg2Ba\n0CwEcFmKcdwH0xrgWwDLAIwE0BHAM1Z8TwPY3DH95TCtAeYDONwxfm+YP9VCAH9wjG8NYKo1/p8A\nesYc/wAAG2Badr0FYLb13W5RC58BwO5WzHMAvA3TDxdqJX7HOg5CvvK5JmKHKaO3t5u59v+wVuK3\nlr8nzIHmHAB/hWmVVEvxtwWwGsCmjnGpxs8L3IiIyIWVz0RE5MLEQERELkwMRETkwsRAREQuTAxE\nROTCxEBERC5MDERE5MLEQERELv8PE6d3QGfYlB4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1068b1198>"
+       "<Figure size 1000x600 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -535,20 +496,50 @@
     }
    ],
    "source": [
-    "plt.plot(res.fun)"
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.subplot(211)\n",
+    "plt.plot(fun0)\n",
+    "plt.title(\"Reprojection errors before optimization, RMS = {:.1f} pixels\".format(np.mean(fun0**2) ** 0.5))\n",
+    "\n",
+    "plt.subplot(212)\n",
+    "plt.plot(res.fun)\n",
+    "plt.title(\"Reprojection errors after optimization, RMS = {:.1f} pixels\".format(np.mean(res.fun**2) ** 0.5))\n",
+    "\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
-    "We see much better picture of residuals now, with the mean being very close to zero. There are some spikes left. It can be explained by outliers in the data, or, possibly, the algorithm found a local minimum (very good one though) or didn't converged enough. Note that the algorithm worked with Jacobian finite difference aproximate, which can potentially block the progress near the minimum because of insufficient accuracy (but again, computing exact Jacobian for this problem is quite difficult)."
+    "We see significant improvements of the reprojection errors distribution and their RMS which indicates a successful optimization.\n",
+    "\n",
+    "The remaining spikes can be explained by some sort of inconsistency in the input data or a compromised convergence of the algorithm. The convergence issues might be caused by finite difference approximation of the Jacobian."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "Suggested excersises for the reader:\n",
+    "\n",
+    "1. Implement analytical Jacobian computation and see if it improves the final cost function and removes the spikes.\n",
+    "2. Try to reduce the final cost function by adjusting the algorithm's parameters.\n",
+    "3. Visualize 3D point clouds before and after optimization"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -562,9 +553,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.3"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }