From 2702c7d7624fc615da777c0cab9cd22ac3d6b87e Mon Sep 17 00:00:00 2001
From: Daniel <daniel@mail.de>
Date: Fri, 28 Jun 2024 15:50:58 +0200
Subject: [PATCH] Greatly improved triangulation.

---
 media/RESULTS.md               | 2604 ++++++++++++++++----------------
 scripts/test_skelda_dataset.py |    4 +-
 scripts/test_triangulate.py    |    4 +-
 scripts/triangulate_poses.py   |  188 ++-
 4 files changed, 1440 insertions(+), 1360 deletions(-)

diff --git a/media/RESULTS.md b/media/RESULTS.md
index 13384ff..9527103 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -4,493 +4,492 @@ Results of the model in various experiments on different datasets.
 
 ### Human36m
 
-(duration 00:01:30)
+(duration 00:01:20)
 ```json
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+  "avg_time_3d": 0.0029713828684919973,
+  "avg_fps": 11.912580036584702
 }
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+  "correct_parts": 8117,
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 }
 ```
 
-
 ### Shelf
 
-(duration 00:01:11)
+(duration 00:00:56)
 ```json
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+  "avg_time_3d": 0.017724498440719553,
+  "avg_fps": 6.020741697901867
 }
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@@ -498,512 +497,512 @@ Results of the model in various experiments on different datasets.
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 {
   "total_parts": 6678,
-  "correct_parts": 6579,
-  "pcp": 0.985175
+  "correct_parts": 6616,
+  "pcp": 0.990716
 }
 ```
 
 ### Campus
 
-(duration 00:00:33)
+(duration 00:00:29)
 ```json
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+    "sem": 0.001516,
     "min": 0.015462,
     "max": 0.210122,
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     "recall-0.1": 0.715426,
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-    "recall-0.5": 0.99734,
+    "recall-0.15": 0.962766,
+    "recall-0.25": 1.0,
+    "recall-0.5": 1.0,
     "num_labels": 376
   },
   "knee_left": {
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+    "median": 0.051737,
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     "min": 0.01259,
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-    "recall-0.05": 0.454787,
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-    "recall-0.15": 0.973404,
-    "recall-0.25": 0.992021,
-    "recall-0.5": 0.99734,
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+    "recall-0.05": 0.465426,
+    "recall-0.1": 0.917553,
+    "recall-0.15": 0.986702,
+    "recall-0.25": 1.0,
+    "recall-0.5": 1.0,
     "num_labels": 376
   },
   "knee_right": {
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-    "sem": 0.001933,
+    "count": 376,
+    "mean": 0.062274,
+    "median": 0.055225,
+    "std": 0.028571,
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     "min": 0.004272,
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     "recall-0.025": 0.015957,
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+    "recall-0.25": 1.0,
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     "num_labels": 376
   },
   "ankle_left": {
-    "count": 374,
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-    "median": 0.058485,
-    "std": 0.061135,
-    "sem": 0.003165,
+    "count": 370,
+    "mean": 0.062845,
+    "median": 0.057915,
+    "std": 0.036038,
+    "sem": 0.001876,
     "min": 0.007413,
-    "max": 0.430472,
+    "max": 0.326849,
     "recall-0.025": 0.074468,
-    "recall-0.05": 0.327128,
-    "recall-0.1": 0.87766,
-    "recall-0.15": 0.941489,
-    "recall-0.25": 0.957447,
-    "recall-0.5": 0.994681,
+    "recall-0.05": 0.335106,
+    "recall-0.1": 0.896277,
+    "recall-0.15": 0.960106,
+    "recall-0.25": 0.976064,
+    "recall-0.5": 0.984043,
     "num_labels": 376
   },
   "ankle_right": {
-    "count": 373,
-    "mean": 0.066713,
-    "median": 0.050668,
-    "std": 0.063969,
-    "sem": 0.003317,
+    "count": 370,
+    "mean": 0.059691,
+    "median": 0.050609,
+    "std": 0.040774,
+    "sem": 0.002123,
     "min": 0.010702,
-    "max": 0.451523,
+    "max": 0.442452,
     "recall-0.025": 0.06117,
-    "recall-0.05": 0.478723,
-    "recall-0.1": 0.888298,
-    "recall-0.15": 0.941489,
-    "recall-0.25": 0.960106,
-    "recall-0.5": 0.992021,
+    "recall-0.05": 0.476064,
+    "recall-0.1": 0.893617,
+    "recall-0.15": 0.960106,
+    "recall-0.25": 0.976064,
+    "recall-0.5": 0.984043,
     "num_labels": 376
   },
   "joint_recalls": {
     "num_labels": 4888,
-    "recall-0.025": 0.04255,
-    "recall-0.05": 0.29419,
-    "recall-0.1": 0.79194,
-    "recall-0.15": 0.9419,
-    "recall-0.25": 0.98343,
-    "recall-0.5": 0.99427
+    "recall-0.025": 0.04317,
+    "recall-0.05": 0.29992,
+    "recall-0.1": 0.80278,
+    "recall-0.15": 0.95479,
+    "recall-0.25": 0.99264,
+    "recall-0.5": 0.99755
   }
 }
 {
   "total_parts": 5264,
-  "correct_parts": 4923,
-  "pcp": 0.93522
+  "correct_parts": 4972,
+  "pcp": 0.944529
 }
 ```
 
 ### MVOR
 
-(duration 00:01:06)
+(duration 00:02:31)
 ```json
 {
-  "avg_time_2d": 0.07443526103153753,
-  "avg_time_3d": 0.00230164943873786,
-  "avg_fps": 13.031538458762391
+  "avg_time_2d": 0.07757738680369634,
+  "avg_time_3d": 0.0026588050922399962,
+  "avg_fps": 12.463203653744767
 }
 {
   "person_nums": {
     "total_frames": 629,
     "total_labels": 1061,
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-    "considered_empty": 257,
-    "valid_preds": 478,
-    "invalid_preds": 63,
-    "missing": 583,
-    "invalid_fraction": 0.11645,
-    "precision": 0.88355,
-    "recall": 0.45052,
-    "f1": 0.59675,
-    "non_empty": 541
+    "total_preds": 761,
+    "considered_empty": 259,
+    "valid_preds": 438,
+    "invalid_preds": 64,
+    "missing": 623,
+    "invalid_fraction": 0.12749,
+    "precision": 0.87251,
+    "recall": 0.41282,
+    "f1": 0.56046,
+    "non_empty": 502
   },
   "mpjpe": {
-    "count": 478,
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-    "std": 0.054626,
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-    "min": 0.04029,
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+    "count": 438,
+    "mean": 0.110999,
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+    "min": 0.041183,
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     "recall-0.025": 0.0,
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-    "recall-0.5": 0.450518,
+    "recall-0.05": 0.00377,
+    "recall-0.1": 0.219604,
+    "recall-0.15": 0.351555,
+    "recall-0.25": 0.402451,
+    "recall-0.5": 0.412818,
     "num_labels": 1061,
     "ap-0.025": 0.0,
-    "ap-0.05": 6.8e-05,
-    "ap-0.1": 0.203258,
-    "ap-0.15": 0.328056,
-    "ap-0.25": 0.395497,
-    "ap-0.5": 0.411448
+    "ap-0.05": 0.000103,
+    "ap-0.1": 0.142082,
+    "ap-0.15": 0.290046,
+    "ap-0.25": 0.373209,
+    "ap-0.5": 0.386698
   },
   "head": {
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   "shoulder_left": {
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   "elbow_left": {
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   "elbow_right": {
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   },
   "wrist_left": {
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     "num_labels": 1061
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   "wrist_right": {
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     "num_labels": 1061
   },
   "hip_left": {
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-    "std": 0.073838,
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+    "count": 431,
+    "mean": 0.190778,
+    "median": 0.168752,
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+    "max": 0.490925,
     "recall-0.025": 0.0,
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-    "recall-0.15": 0.172479,
-    "recall-0.25": 0.39114,
-    "recall-0.5": 0.446748,
+    "recall-0.05": 0.000943,
+    "recall-0.1": 0.0377,
+    "recall-0.15": 0.147031,
+    "recall-0.25": 0.321395,
+    "recall-0.5": 0.406221,
     "num_labels": 1061
   },
   "hip_right": {
-    "count": 472,
-    "mean": 0.170201,
-    "median": 0.156315,
-    "std": 0.068564,
-    "sem": 0.003159,
-    "min": 0.018719,
-    "max": 0.448139,
-    "recall-0.025": 0.000943,
-    "recall-0.05": 0.001885,
-    "recall-0.1": 0.053723,
-    "recall-0.15": 0.199811,
-    "recall-0.25": 0.392083,
-    "recall-0.5": 0.444863,
+    "count": 430,
+    "mean": 0.188866,
+    "median": 0.170728,
+    "std": 0.083464,
+    "sem": 0.00403,
+    "min": 0.053341,
+    "max": 0.489645,
+    "recall-0.025": 0.0,
+    "recall-0.05": 0.0,
+    "recall-0.1": 0.04524,
+    "recall-0.15": 0.150801,
+    "recall-0.25": 0.328935,
+    "recall-0.5": 0.405278,
     "num_labels": 1061
   },
   "knee_left": {},
@@ -1012,831 +1011,830 @@ Results of the model in various experiments on different datasets.
   "ankle_right": {},
   "joint_recalls": {
     "num_labels": 9549,
-    "recall-0.025": 0.05603,
-    "recall-0.05": 0.17269,
-    "recall-0.1": 0.27951,
-    "recall-0.15": 0.34737,
-    "recall-0.25": 0.42015,
-    "recall-0.5": 0.44602
+    "recall-0.025": 0.05435,
+    "recall-0.05": 0.15415,
+    "recall-0.1": 0.24736,
+    "recall-0.15": 0.30003,
+    "recall-0.25": 0.37061,
+    "recall-0.5": 0.40873
   }
 }
 {
   "total_parts": 10610,
-  "correct_parts": 3884,
-  "pcp": 0.36607
+  "correct_parts": 3342,
+  "pcp": 0.314986
 }
 ```
 
-
 ### Panoptic
 
-(duration 00:02:52)
+(duration 00:02:28)
 ```json
 {
-  "avg_time_2d": 0.13794401273494813,
-  "avg_time_3d": 0.02159386669717184,
-  "avg_fps": 6.268103873259009
+  "avg_time_2d": 0.15851887609900497,
+  "avg_time_3d": 0.02711676795308183,
+  "avg_fps": 5.3868964934310455
 }
 {
   "person_nums": {
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     "total_labels": 1466,
-    "total_preds": 1954,
-    "considered_empty": 183,
-    "valid_preds": 1450,
-    "invalid_preds": 321,
-    "missing": 16,
-    "invalid_fraction": 0.18125,
-    "precision": 0.81875,
-    "recall": 0.98909,
-    "f1": 0.89589,
-    "non_empty": 1771
+    "total_preds": 1738,
+    "considered_empty": 52,
+    "valid_preds": 1454,
+    "invalid_preds": 232,
+    "missing": 12,
+    "invalid_fraction": 0.1376,
+    "precision": 0.8624,
+    "recall": 0.99181,
+    "f1": 0.92259,
+    "non_empty": 1686
   },
   "mpjpe": {
-    "count": 1450,
-    "mean": 0.060758,
-    "median": 0.05923,
-    "std": 0.022,
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-    "min": 0.021858,
-    "max": 0.179949,
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-    "recall-0.1": 0.938608,
-    "recall-0.15": 0.988404,
-    "recall-0.25": 0.989086,
-    "recall-0.5": 0.989086,
+    "count": 1454,
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+    "recall-0.15": 0.99045,
+    "recall-0.25": 0.991814,
+    "recall-0.5": 0.991814,
     "num_labels": 1466,
-    "ap-0.025": 0.001222,
-    "ap-0.05": 0.266431,
-    "ap-0.1": 0.883457,
-    "ap-0.15": 0.935971,
-    "ap-0.25": 0.937574,
-    "ap-0.5": 0.937574
+    "ap-0.025": 0.060414,
+    "ap-0.05": 0.680845,
+    "ap-0.1": 0.943244,
+    "ap-0.15": 0.973463,
+    "ap-0.25": 0.974787,
+    "ap-0.5": 0.974787
   },
   "nose": {
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-    "recall-0.1": 0.971975,
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   "joint_recalls": {
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 {
   "total_parts": 20444,
-  "correct_parts": 19566,
-  "pcp": 0.957053
+  "correct_parts": 19848,
+  "pcp": 0.970847
 }
 ```
 
 ### IkeaAsm
 
-(duration 00:01:20)
+(duration 00:01:45)
 ```json
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+  "avg_time_3d": 0.00213554522974028,
+  "avg_fps": 13.785141024842861
 }
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 {
   "total_parts": 2243,
-  "correct_parts": 1901,
-  "pcp": 0.847526
+  "correct_parts": 1906,
+  "pcp": 0.849755
 }
 ```
 
 ### Tsinghua
 
-(duration 00:03:02)
+(duration 00:01:51)
 ```json
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+  "avg_time_3d": 0.017471200227737425,
+  "avg_fps": 5.679269596161257
 }
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-    "recall-0.25": 0.980745,
+    "mean": 0.045965,
+    "median": 0.043071,
+    "std": 0.02706,
+    "sem": 0.000973,
+    "min": 0.005523,
+    "max": 0.362978,
+    "recall-0.025": 0.184852,
+    "recall-0.05": 0.634146,
+    "recall-0.1": 0.970475,
+    "recall-0.15": 0.984596,
+    "recall-0.25": 0.993582,
     "recall-0.5": 0.994865,
     "num_labels": 779
   },
   "knee_right": {
-    "count": 770,
-    "mean": 0.054802,
-    "median": 0.047066,
-    "std": 0.049371,
-    "sem": 0.00178,
-    "min": 0.006292,
-    "max": 0.480681,
-    "recall-0.025": 0.125323,
-    "recall-0.05": 0.556848,
-    "recall-0.1": 0.939276,
-    "recall-0.15": 0.976744,
-    "recall-0.25": 0.98062,
-    "recall-0.5": 0.994832,
+    "count": 771,
+    "mean": 0.049529,
+    "median": 0.046727,
+    "std": 0.024371,
+    "sem": 0.000878,
+    "min": 0.006187,
+    "max": 0.178582,
+    "recall-0.025": 0.134367,
+    "recall-0.05": 0.578811,
+    "recall-0.1": 0.96124,
+    "recall-0.15": 0.989664,
+    "recall-0.25": 0.996124,
+    "recall-0.5": 0.996124,
     "num_labels": 774
   },
   "ankle_left": {
-    "count": 773,
-    "mean": 0.051212,
-    "median": 0.042763,
-    "std": 0.052084,
-    "sem": 0.001875,
-    "min": 0.000608,
-    "max": 0.499443,
-    "recall-0.025": 0.184852,
-    "recall-0.05": 0.636714,
-    "recall-0.1": 0.942234,
-    "recall-0.15": 0.960205,
-    "recall-0.25": 0.976893,
-    "recall-0.5": 0.992298,
+    "count": 774,
+    "mean": 0.040168,
+    "median": 0.037831,
+    "std": 0.021928,
+    "sem": 0.000789,
+    "min": 0.002465,
+    "max": 0.213487,
+    "recall-0.025": 0.2362,
+    "recall-0.05": 0.716303,
+    "recall-0.1": 0.980745,
+    "recall-0.15": 0.988447,
+    "recall-0.25": 0.993582,
+    "recall-0.5": 0.993582,
     "num_labels": 779
   },
   "ankle_right": {
-    "count": 760,
-    "mean": 0.049765,
-    "median": 0.041668,
-    "std": 0.044599,
-    "sem": 0.001619,
-    "min": 0.003368,
-    "max": 0.458733,
-    "recall-0.025": 0.206494,
-    "recall-0.05": 0.64026,
-    "recall-0.1": 0.927273,
-    "recall-0.15": 0.964935,
-    "recall-0.25": 0.977922,
-    "recall-0.5": 0.987013,
+    "count": 769,
+    "mean": 0.043193,
+    "median": 0.039079,
+    "std": 0.025619,
+    "sem": 0.000924,
+    "min": 0.002305,
+    "max": 0.189483,
+    "recall-0.025": 0.216883,
+    "recall-0.05": 0.694805,
+    "recall-0.1": 0.963636,
+    "recall-0.15": 0.990909,
+    "recall-0.25": 0.998701,
+    "recall-0.5": 0.998701,
     "num_labels": 770
   },
   "joint_recalls": {
     "num_labels": 10047,
-    "recall-0.025": 0.09515,
-    "recall-0.05": 0.45884,
-    "recall-0.1": 0.90206,
-    "recall-0.15": 0.9575,
-    "recall-0.25": 0.97691,
-    "recall-0.5": 0.99303
+    "recall-0.025": 0.12292,
+    "recall-0.05": 0.55021,
+    "recall-0.1": 0.94685,
+    "recall-0.15": 0.98427,
+    "recall-0.25": 0.99343,
+    "recall-0.5": 0.99472
   }
 }
 {
   "total_parts": 10800,
-  "correct_parts": 10401,
-  "pcp": 0.963056
+  "correct_parts": 10664,
+  "pcp": 0.987407
 }
 ```
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index c2fdc4e..3f674c3 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -357,7 +357,9 @@ def main():
             poses3D = np.zeros([1, len(joint_names_3d), 4])
             poses2D = np.zeros([len(images_2d), 1, len(joint_names_3d), 3])
         else:
-            poses3D = triangulate_poses.get_3d_pose(poses_2d, label["cameras"], joint_names_2d)
+            poses3D = triangulate_poses.get_3d_pose(
+                poses_2d, label["cameras"], roomparams, joint_names_2d
+            )
             poses2D = []
             for cam in label["cameras"]:
                 poses_2d, _ = utils_pose.project_poses(poses3D, cam)
diff --git a/scripts/test_triangulate.py b/scripts/test_triangulate.py
index aef1851..7b9e215 100644
--- a/scripts/test_triangulate.py
+++ b/scripts/test_triangulate.py
@@ -439,7 +439,9 @@ def main():
             poses3D = np.zeros([1, len(joint_names_3d), 4])
             poses2D = np.zeros([len(images_2d), 1, len(joint_names_3d), 3])
         else:
-            poses3D = triangulate_poses.get_3d_pose(poses_2d, camparams, joint_names_2d)
+            poses3D = triangulate_poses.get_3d_pose(
+                poses_2d, camparams, roomparams, joint_names_2d
+            )
             poses2D = []
             for cam in camparams:
                 poses_2d, _ = utils_pose.project_poses(poses3D, cam)
diff --git a/scripts/triangulate_poses.py b/scripts/triangulate_poses.py
index 0af75ae..a627be2 100644
--- a/scripts/triangulate_poses.py
+++ b/scripts/triangulate_poses.py
@@ -1,4 +1,6 @@
 import copy
+import math
+import time
 
 import cv2
 import numpy as np
@@ -8,8 +10,6 @@ from skelda import utils_pose
 # ==================================================================================================
 
 core_joints = [
-    "shoulder_middle",
-    "hip_middle",
     "shoulder_left",
     "shoulder_right",
     "hip_left",
@@ -64,7 +64,7 @@ def get_camera_P(cam):
 # ==================================================================================================
 
 
-def calc_pair_score(pair, poses_2d, camparams, joint_names_2d):
+def calc_pair_score(pair, poses_2d, camparams, roomparams, joint_names_2d, use_joints):
     """Triangulates a pair of persons and scores them based on the reprojection error"""
 
     cam1 = camparams[pair[0][0]]
@@ -73,45 +73,77 @@ def calc_pair_score(pair, poses_2d, camparams, joint_names_2d):
     pose2 = np.array(poses_2d[pair[0][1]][pair[0][3]])
 
     # Select core joints
-    jids = [joint_names_2d.index(j) for j in core_joints]
+    jids = [joint_names_2d.index(j) for j in use_joints]
     pose1 = pose1[jids]
     pose2 = pose2[jids]
 
-    poses_3d, score = calc_pose_scored(pose1, pose2, cam1, cam2)
+    poses_3d, score = calc_pose_scored(pose1, pose2, cam1, cam2, roomparams)
     return poses_3d, score
 
 
 # ==================================================================================================
 
 
-def calc_pose_scored(pose1, pose2, cam1, cam2):
+def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
     """Triangulates a pair of persons and scores them based on the reprojection error"""
 
+    # Mask out invisible joints
+    mask1a = pose1[:, 2] >= 0.1
+    mask2a = pose2[:, 2] >= 0.1
+    mask = mask1a & mask2a
+
+    # If no joints are visible return a low score
+    if np.sum(mask) == 0:
+        pose3d = np.zeros([len(pose1), 4])
+        score = 0.0
+        return pose3d, score
+
     # Triangulate points
-    points1 = pose1[:, 0:2].T
-    points2 = pose2[:, 0:2].T
+    points1 = pose1[mask, 0:2].T
+    points2 = pose2[mask, 0:2].T
     P1 = get_camera_P(cam1)
     P2 = get_camera_P(cam2)
     points3d = cv2.triangulatePoints(P1, P2, points1, points2)
     points3d = points3d / points3d[3, :]
     points3d = points3d[0:3, :].T
+    pose3d = np.zeros([len(pose1), 4])
+    pose3d[mask] = np.concatenate([points3d, np.ones([points3d.shape[0], 1])], axis=-1)
+
+    # If the triangulated points are outside the room drop it
+    mean = np.mean(pose3d[mask][:, 0:3], axis=0)
+    mins = np.min(pose3d[mask][:, 0:3], axis=0)
+    maxs = np.max(pose3d[mask][:, 0:3], axis=0)
+    rsize = np.array(roomparams["room_size"]) / 2
+    rcent = np.array(roomparams["room_center"])
+    center_outside = np.any((mean > rsize + rcent) | (mean < -rsize + rcent))
+    limb_outside = np.any((maxs > rsize + rcent + 0.1) | (mins < -rsize + rcent - 0.1))
+    if center_outside or limb_outside:
+        pose3d[:, 3] = 0.001
+        score = 0.001
+        return pose3d, score
 
     # Calculate reprojection error
-    poses_3d = np.expand_dims(points3d, axis=0)
-    poses_3d = np.concatenate([poses_3d, np.ones([1, poses_3d.shape[1], 1])], axis=-1)
+    poses_3d = np.expand_dims(pose3d, axis=0)
     repro1, _ = utils_pose.project_poses(poses_3d, cam1)
     repro2, _ = utils_pose.project_poses(poses_3d, cam2)
     repro1 = repro1[0]
     repro2 = repro2[0]
-    mask1 = pose1[:, 2] > 0.1
-    mask2 = pose2[:, 2] > 0.1
-    mask = mask1 & mask2
     error1 = np.linalg.norm(pose1[mask, 0:2] - repro1[mask, 0:2], axis=1)
     error2 = np.linalg.norm(pose2[mask, 0:2] - repro2[mask, 0:2], axis=1)
 
+    # Set errors of invisible reprojections to a high value
+    penalty = (cam1["width"] + cam1["height"]) / 2
+    mask1b = (repro1[:, 2] < 0.1)[mask]
+    mask2b = (repro2[:, 2] < 0.1)[mask]
+    error1[mask1b] = penalty
+    error2[mask2b] = penalty
+
     # Convert errors to a score
-    error1 = error1 / ((cam1["width"] + cam1["height"]) / 2)
-    error2 = error2 / ((cam2["width"] + cam2["height"]) / 2)
+    scale = (cam1["width"] + cam1["height"]) / 2
+    error1 = error1.clip(0, scale / 4)
+    error2 = error2.clip(0, scale / 4)
+    error1 = error1 / scale
+    error2 = error2 / scale
     error = (error1 + error2) / 2
     scores = 1.0 / (1.0 + error * 10)
     score = np.mean(scores)
@@ -119,9 +151,9 @@ def calc_pose_scored(pose1, pose2, cam1, cam2):
     # Add score to 3D pose
     full_scores = np.zeros([poses_3d.shape[1], 1])
     full_scores[mask] = np.expand_dims(scores, axis=-1)
-    pose_3d = np.concatenate([points3d, full_scores], axis=-1)
+    pose3d[:, 3] = full_scores[:, 0]
 
-    return pose_3d, score
+    return pose3d, score
 
 
 # ==================================================================================================
@@ -131,12 +163,13 @@ def calc_grouping(all_pairs):
     """Groups pairs that share a person"""
 
     # Calculate the pose center for each pair
+    min_score = 0.9
     for i in range(len(all_pairs)):
         pair = all_pairs[i]
         pose_3d = pair[2][0]
-        mask = pose_3d[:, 2] > 0.1
+        mask = pose_3d[:, 3] > min_score
         center = np.mean(pose_3d[mask, 0:3], axis=0)
-        all_pairs[i] = all_pairs[i] + (center,)
+        all_pairs[i] = all_pairs[i] + [center]
 
     groups = []
     for i in range(len(all_pairs)):
@@ -144,33 +177,43 @@ def calc_grouping(all_pairs):
 
         # Create new group if non exists
         if len(groups) == 0:
-            groups.append([pair])
+            groups.append([pair[4], pair[2][0], [pair]])
             continue
 
-        # Check if the pair belongs to an existing group
-        matched = False
+        # Check if the pair matches to an existing group
+        max_center_dist = 0.9
+        max_joint_avg_dist = 0.3
+        best_dist = math.inf
+        best_group = -1
         for j in range(len(groups)):
-            g0 = groups[j][0]
-            center0 = g0[3]
-            center1 = pair[3]
-            if np.linalg.norm(center0 - center1) < 0.5:
-                pose0 = g0[2][0]
+            g0 = groups[j]
+            center0 = g0[0]
+            center1 = pair[4]
+            if np.linalg.norm(center0 - center1) < max_center_dist:
+                pose0 = g0[1]
                 pose1 = pair[2][0]
 
                 # Calculate the distance between the two poses
-                mask0 = pose0[:, 3] > 0.1
-                mask1 = pose1[:, 3] > 0.1
+                mask0 = pose0[:, 3] > min_score
+                mask1 = pose1[:, 3] > min_score
                 mask = mask0 & mask1
                 dists = np.linalg.norm(pose0[mask, 0:3] - pose1[mask, 0:3], axis=1)
                 dist = np.mean(dists)
-                if dist < 0.3:
-                    groups[j].append(pair)
-                    matched = True
-                    break
-
-        # Create new group if no match was found
-        if not matched:
-            groups.append([pair])
+                if dist < max_joint_avg_dist:
+                    if dist < best_dist:
+                        best_dist = dist
+                        best_group = j
+        if best_group >= 0:
+            # Add pair to existing group and update the mean positions
+            group = groups[best_group]
+            new_center = (group[0] * len(group[1]) + pair[4]) / (len(group[1]) + 1)
+            new_pose = (group[1] * len(group[1]) + pair[2][0]) / (len(group[1]) + 1)
+            group[2].append(pair)
+            group[0] = new_center
+            group[1] = new_pose
+        else:
+            # Create new group if no match was found
+            groups.append([pair[4], pair[2][0], [pair]])
 
     return groups
 
@@ -178,30 +221,51 @@ def calc_grouping(all_pairs):
 # ==================================================================================================
 
 
-def merge_group(group, poses_2d, camparams):
+def merge_group(poses_3d: np.ndarray):
     """Merges a group of poses into a single pose"""
 
-    # Calculate full 3D poses
-    poses_3d = []
-    for pair in group:
-        cam1 = camparams[pair[0][0]]
-        cam2 = camparams[pair[0][1]]
-        pose1 = np.array(poses_2d[pair[0][0]][pair[0][2]])
-        pose2 = np.array(poses_2d[pair[0][1]][pair[0][3]])
+    # Merge poses to create initial pose
+    # Use only those triangulations with a high score
+    min_score = 0.9
+    mask = poses_3d[:, :, 3:4] > min_score
+    sum_poses = np.sum(poses_3d * mask, axis=0)
+    sum_mask = np.sum(mask, axis=0)
+    initial_pose_3d = np.divide(
+        sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
+    )
 
-        pose_3d, _ = calc_pose_scored(pose1, pose2, cam1, cam2)
-        poses_3d.append(pose_3d)
+    # Drop outliers that are far away from the other proposals
+    max_dist = 0.3
+    distances = np.linalg.norm(
+        poses_3d[:, :, :3] - initial_pose_3d[np.newaxis, :, :3], axis=2
+    )
+    dist_mask = distances <= max_dist
+    mask = mask & np.expand_dims(dist_mask, axis=-1)
 
-    # Merge poses
-    pose_3d = np.mean(poses_3d, axis=0)
+    # Select the best-k proposals for each joint that are closest to the initial pose
+    keep_best = 3
+    sorted_indices = np.argsort(distances, axis=0)
+    best_k_mask = np.zeros_like(mask, dtype=bool)
+    num_joints = poses_3d.shape[1]
+    for i in range(num_joints):
+        valid_indices = sorted_indices[:, i][mask[sorted_indices[:, i], i, 0]]
+        best_k_mask[valid_indices[:keep_best], i, 0] = True
+    mask = mask & best_k_mask
 
-    return pose_3d
+    # Final pose computation with combined masks
+    sum_poses = np.sum(poses_3d * mask, axis=0)
+    sum_mask = np.sum(mask, axis=0)
+    final_pose_3d = np.divide(
+        sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
+    )
+
+    return final_pose_3d
 
 
 # ==================================================================================================
 
 
-def get_3d_pose(poses_2d, camparams, joint_names_2d):
+def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d):
 
     # Undistort 2D points
     for i, cam in enumerate(camparams):
@@ -220,13 +284,15 @@ def get_3d_pose(poses_2d, camparams, joint_names_2d):
                 for l in range(len(poses2)):
                     pid1 = sum(num_persons[:i]) + k
                     pid2 = sum(num_persons[:j]) + l
-                    all_pairs.append(((i, j, k, l), (pid1, pid2)))
+                    all_pairs.append([(i, j, k, l), (pid1, pid2)])
 
     # Calculate pair scores
     for i in range(len(all_pairs)):
         pair = all_pairs[i]
-        pose_3d, score = calc_pair_score(pair, poses_2d, camparams, joint_names_2d)
-        all_pairs[i] = all_pairs[i] + ((pose_3d, score),)
+        pose_3d, score = calc_pair_score(
+            pair, poses_2d, camparams, roomparams, joint_names_2d, core_joints
+        )
+        all_pairs[i].append((pose_3d, score))
 
         # import draw_utils
         # poses3D = np.array([pose_3d])
@@ -239,13 +305,25 @@ def get_3d_pose(poses_2d, camparams, joint_names_2d):
     min_score = 0.9
     all_pairs = [p for p in all_pairs if p[2][1] > min_score]
 
+    # Calculate full 3D poses
+    poses_3d = []
+    for pair in all_pairs:
+        cam1 = camparams[pair[0][0]]
+        cam2 = camparams[pair[0][1]]
+        pose1 = np.array(poses_2d[pair[0][0]][pair[0][2]])
+        pose2 = np.array(poses_2d[pair[0][1]][pair[0][3]])
+
+        pose_3d, _ = calc_pose_scored(pose1, pose2, cam1, cam2, roomparams)
+        pair.append(pose_3d)
+
     # Group pairs that share a person
     groups = calc_grouping(all_pairs)
 
     # Merge groups
     poses_3d = []
     for group in groups:
-        pose_3d = merge_group(group, poses_2d, camparams)
+        poses = np.array([p[3] for p in group[2]])
+        pose_3d = merge_group(poses)
         poses_3d.append(pose_3d)
 
     if len(poses_3d) > 0: