Add MOM description to README and call in library

Author: anastasiia-kornilova

docs/usage.rst

@@ -18,25 +18,49 @@ Mean Map Entropy calculcates average entropy of every point vicinity in the aggr
 
     H(P) = \frac{1}{|P|}\sum_{k=1}^{|P|} h(q_k)
 
-To use it, provide `pcs` --- a list of point clouds pcs in form of `open3d.PointCloud` and `Ts` --- a list of 
-corresponding poses in the trajectory in form of `4x4` transformation matrices.
+To use it, provide `pcs` --- a list of point clouds pcs in the form of `open3d.PointCloud` and `Ts` --- a list of 
+corresponding poses in the trajectory in the form of `4x4` transformation matrices.
 
 .. code-block:: python
 
     map_metrics.mme(pcs, Ts)
 
 
 Mean Plane Variance (MPV)
-----------------------
+-------------------------
 
 Mean Plane Variance calculcates average plane variance of every point vicinity in the aggregated map:
 
 .. math::
     V(P) = \frac{1}{|P|}\sum_{k=1}^{|P|}v(p_k) = \frac{1}{|P|}\sum_{k=1}^{|P|} \lambda_{min}
 
-To use it, provide `pcs` --- a list of point clouds pcs in form of `open3d.PointCloud` and `Ts` --- a list of 
-corresponding poses in the trajectory in form of `4x4` transformation matrices.
+To use it, provide `pcs` --- a list of point clouds pcs in the form of `open3d.PointCloud` and `Ts` --- a list of 
+corresponding poses in the trajectory in the form of `4x4` transformation matrices.
 
 .. code-block:: python
 
     map_metrics.mpv(pcs, Ts)
+
+
+Mutually Orthogonal Metric (MOM)
+--------------------------------
+
+Mutual Orthogonality is a concept of considering not all points in the map but only ones from mutually orthogonal 
+surfaces. Mean Plane Variance over those points provides (as described in our paper) correlation with Relative Pose 
+Error (RPE) --- one of the popular full-reference metrics for trajectories.
+
+To use it, provide `pcs` --- a list of point clouds pcs in the form of `open3d.PointCloud` and `Ts` --- a list of 
+corresponding poses in the trajectory in the form of `4x4` transformation matrices.
+
+.. code-block:: python
+    
+    map_metrics.mom(pcs, Ts)
+
+The default usage of the method assumes extraction of points from mutually orthogonal surfaces during the method 
+execution and therefore increase calculation time. One can extract those points manually one time for specific set 
+of point cloud and use it as parameter to calculate MOM faster.
+
+.. code-block:: python
+    
+    orth_list, _, _ = map_metrics.extract_orthogonal_subsets(pcs[0])
+    print(map_metrics.orth_mme(pcs, Ts_gt, orth_list=orth_list))

map_metrics/map_metrics.py

@@ -62,6 +62,10 @@ def mpv(pcs, Ts):
     return 0 if len(metric) == 0 else np.mean(metric)
 
 
+def mom(pcs, Ts, orth_list=None):
+    return orth_mme(pcs, Ts, orth_list=None)
+
+
 def extract_orthogonal_subsets(pc, eps=1e-1, vis=False):
     # Estimate normals if they are not calculated for pc
     if not pc.has_normals():