diff --git a/media/2d-k.png b/media/2d-k.png
index f569cf9..4f31a50 100644
Binary files a/media/2d-k.png and b/media/2d-k.png differ
diff --git a/media/2d-p.png b/media/2d-p.png
index d3a7359..199c188 100644
Binary files a/media/2d-p.png and b/media/2d-p.png differ
diff --git a/media/3d-p.png b/media/3d-p.png
index 4df687b..a08aa06 100644
Binary files a/media/3d-p.png and b/media/3d-p.png differ
diff --git a/media/RESULTS.md b/media/RESULTS.md
index 9527103..f346c19 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -7,218 +7,218 @@ Results of the model in various experiments on different datasets.
 (duration 00:01:20)
 ```json
 {
-  "avg_time_2d": 0.08097348859754659,
-  "avg_time_3d": 0.0029713828684919973,
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+  "avg_time_2d": 0.08300593747931012,
+  "avg_time_3d": 0.0030075873358774994,
+  "avg_fps": 11.62607859808842
 }
 {
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@@ -226,50 +226,50 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_left": {
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+    "recall-0.25": 0.99679,
     "recall-0.5": 1.0
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 }
 {
   "total_parts": 8400,
-  "correct_parts": 8117,
-  "pcp": 0.96631
+  "correct_parts": 8120,
+  "pcp": 0.966667
 }
 ```
 
@@ -278,58 +278,58 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:56)
 ```json
 {
-  "avg_time_2d": 0.14836799483938315,
-  "avg_time_3d": 0.017724498440719553,
-  "avg_fps": 6.020741697901867
+  "avg_time_2d": 0.14058070084483354,
+  "avg_time_3d": 0.016990919703060818,
+  "avg_fps": 6.346320463817576
 }
 {
   "person_nums": {
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     "valid_preds": 477,
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     "missing": 0,
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@@ -337,47 +337,47 @@ Results of the model in various experiments on different datasets.
   },
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@@ -385,63 +385,63 @@ Results of the model in various experiments on different datasets.
   },
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@@ -449,31 +449,31 @@ Results of the model in various experiments on different datasets.
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@@ -481,15 +481,15 @@ Results of the model in various experiments on different datasets.
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@@ -497,14 +497,14 @@ Results of the model in various experiments on different datasets.
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@@ -513,34 +513,34 @@ Results of the model in various experiments on different datasets.
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-  "correct_parts": 6616,
-  "pcp": 0.990716
+  "correct_parts": 6618,
+  "pcp": 0.991015
 }
 ```
 
@@ -549,58 +549,58 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:29)
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+  "correct_parts": 4925,
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 }
 ```
 
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   "knee_left": {},
@@ -1011,18 +1011,18 @@ Results of the model in various experiments on different datasets.
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-  "correct_parts": 3342,
-  "pcp": 0.314986
+  "correct_parts": 5946,
+  "pcp": 0.560415
 }
 ```
 
@@ -1031,269 +1031,269 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:28)
 ```json
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 {
   "total_parts": 20444,
-  "correct_parts": 19848,
-  "pcp": 0.970847
+  "correct_parts": 19868,
+  "pcp": 0.971825
 }
 ```
 
@@ -1310,261 +1310,261 @@ Results of the model in various experiments on different datasets.
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-    "recall-0.25": 0.857143,
-    "recall-0.5": 0.894737,
+    "recall-0.05": 0.496241,
+    "recall-0.1": 0.721805,
+    "recall-0.15": 0.857143,
+    "recall-0.25": 0.887218,
+    "recall-0.5": 0.93985,
     "num_labels": 133
   },
   "ankle_left": {
-    "count": 91,
-    "mean": 0.138874,
-    "median": 0.110764,
-    "std": 0.111323,
-    "sem": 0.011734,
+    "count": 95,
+    "mean": 0.161343,
+    "median": 0.131309,
+    "std": 0.121429,
+    "sem": 0.012524,
     "min": 0.008477,
-    "max": 0.434693,
+    "max": 0.452581,
     "recall-0.025": 0.052174,
-    "recall-0.05": 0.191304,
-    "recall-0.1": 0.382609,
-    "recall-0.15": 0.504348,
-    "recall-0.25": 0.669565,
-    "recall-0.5": 0.791304,
+    "recall-0.05": 0.147826,
+    "recall-0.1": 0.33913,
+    "recall-0.15": 0.495652,
+    "recall-0.25": 0.626087,
+    "recall-0.5": 0.826087,
     "num_labels": 115
   },
   "ankle_right": {
-    "count": 68,
-    "mean": 0.110192,
-    "median": 0.071169,
-    "std": 0.111019,
-    "sem": 0.013563,
+    "count": 78,
+    "mean": 0.151625,
+    "median": 0.081017,
+    "std": 0.148102,
+    "sem": 0.016878,
     "min": 0.01164,
     "max": 0.47701,
     "recall-0.025": 0.12766,
-    "recall-0.05": 0.287234,
+    "recall-0.05": 0.276596,
     "recall-0.1": 0.457447,
-    "recall-0.15": 0.56383,
-    "recall-0.25": 0.638298,
-    "recall-0.5": 0.723404,
+    "recall-0.15": 0.574468,
+    "recall-0.25": 0.62766,
+    "recall-0.5": 0.829787,
     "num_labels": 94
   },
   "joint_recalls": {
     "num_labels": 2490,
-    "recall-0.025": 0.29598,
-    "recall-0.05": 0.55502,
-    "recall-0.1": 0.75141,
-    "recall-0.15": 0.81406,
-    "recall-0.25": 0.84297,
-    "recall-0.5": 0.86827
+    "recall-0.025": 0.3008,
+    "recall-0.05": 0.56627,
+    "recall-0.1": 0.77309,
+    "recall-0.15": 0.8498,
+    "recall-0.25": 0.87992,
+    "recall-0.5": 0.91888
   }
 }
 {
   "total_parts": 2243,
-  "correct_parts": 1906,
-  "pcp": 0.849755
+  "correct_parts": 1980,
+  "pcp": 0.882746
 }
 ```
 
@@ -1581,260 +1581,260 @@ Results of the model in various experiments on different datasets.
   "person_nums": {
     "total_frames": 330,
     "total_labels": 780,
-    "total_preds": 1057,
-    "considered_empty": 23,
-    "valid_preds": 776,
-    "invalid_preds": 258,
-    "missing": 4,
-    "invalid_fraction": 0.24952,
-    "precision": 0.75048,
-    "recall": 0.99487,
-    "f1": 0.85557,
-    "non_empty": 1034
+    "total_preds": 852,
+    "considered_empty": 2,
+    "valid_preds": 777,
+    "invalid_preds": 73,
+    "missing": 3,
+    "invalid_fraction": 0.08588,
+    "precision": 0.91412,
+    "recall": 0.99615,
+    "f1": 0.95337,
+    "non_empty": 850
   },
   "mpjpe": {
-    "count": 776,
-    "mean": 0.051933,
-    "median": 0.049863,
-    "std": 0.014382,
-    "sem": 0.000517,
-    "min": 0.029634,
-    "max": 0.290459,
+    "count": 777,
+    "mean": 0.053355,
+    "median": 0.050846,
+    "std": 0.021626,
+    "sem": 0.000776,
+    "min": 0.030764,
+    "max": 0.388758,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.501282,
-    "recall-0.1": 0.992308,
-    "recall-0.15": 0.99359,
+    "recall-0.05": 0.462821,
+    "recall-0.1": 0.989744,
+    "recall-0.15": 0.991026,
     "recall-0.25": 0.99359,
-    "recall-0.5": 0.994872,
+    "recall-0.5": 0.996154,
     "num_labels": 780,
     "ap-0.025": 0.0,
-    "ap-0.05": 0.306795,
-    "ap-0.1": 0.991081,
-    "ap-0.15": 0.99261,
-    "ap-0.25": 0.99261,
-    "ap-0.5": 0.993698
+    "ap-0.05": 0.266025,
+    "ap-0.1": 0.985969,
+    "ap-0.15": 0.987296,
+    "ap-0.25": 0.989637,
+    "ap-0.5": 0.993161
   },
   "head": {
     "count": 753,
-    "mean": 0.03895,
-    "median": 0.038185,
-    "std": 0.016016,
-    "sem": 0.000584,
-    "min": 0.004795,
-    "max": 0.097068,
-    "recall-0.025": 0.215139,
-    "recall-0.05": 0.77822,
-    "recall-0.1": 1.0,
-    "recall-0.15": 1.0,
-    "recall-0.25": 1.0,
+    "mean": 0.040928,
+    "median": 0.039126,
+    "std": 0.022925,
+    "sem": 0.000836,
+    "min": 0.00567,
+    "max": 0.385126,
+    "recall-0.025": 0.181939,
+    "recall-0.05": 0.741036,
+    "recall-0.1": 0.997344,
+    "recall-0.15": 0.997344,
+    "recall-0.25": 0.997344,
     "recall-0.5": 1.0,
     "num_labels": 753
   },
   "shoulder_left": {
-    "count": 776,
-    "mean": 0.061926,
-    "median": 0.061105,
-    "std": 0.020617,
-    "sem": 0.000741,
-    "min": 0.019191,
-    "max": 0.220586,
-    "recall-0.025": 0.008974,
+    "count": 777,
+    "mean": 0.062522,
+    "median": 0.059746,
+    "std": 0.024296,
+    "sem": 0.000872,
+    "min": 0.022216,
+    "max": 0.355614,
+    "recall-0.025": 0.003846,
     "recall-0.05": 0.308974,
-    "recall-0.1": 0.939744,
+    "recall-0.1": 0.941026,
     "recall-0.15": 0.99359,
-    "recall-0.25": 0.994872,
-    "recall-0.5": 0.994872,
+    "recall-0.25": 0.99359,
+    "recall-0.5": 0.996154,
     "num_labels": 780
   },
   "shoulder_right": {
-    "count": 776,
-    "mean": 0.064914,
-    "median": 0.060649,
-    "std": 0.022005,
-    "sem": 0.00079,
+    "count": 777,
+    "mean": 0.067595,
+    "median": 0.062627,
+    "std": 0.030316,
+    "sem": 0.001088,
     "min": 0.022078,
-    "max": 0.150885,
+    "max": 0.491641,
     "recall-0.025": 0.002564,
-    "recall-0.05": 0.283333,
-    "recall-0.1": 0.919231,
-    "recall-0.15": 0.99359,
-    "recall-0.25": 0.994872,
-    "recall-0.5": 0.994872,
+    "recall-0.05": 0.258974,
+    "recall-0.1": 0.901282,
+    "recall-0.15": 0.991026,
+    "recall-0.25": 0.99359,
+    "recall-0.5": 0.996154,
     "num_labels": 780
   },
   "elbow_left": {
-    "count": 771,
-    "mean": 0.048155,
-    "median": 0.044396,
-    "std": 0.022235,
-    "sem": 0.000801,
-    "min": 0.003098,
-    "max": 0.21128,
-    "recall-0.025": 0.108247,
-    "recall-0.05": 0.597938,
+    "count": 773,
+    "mean": 0.048114,
+    "median": 0.044687,
+    "std": 0.022592,
+    "sem": 0.000813,
+    "min": 0.00551,
+    "max": 0.209571,
+    "recall-0.025": 0.113402,
+    "recall-0.05": 0.614691,
     "recall-0.1": 0.972938,
-    "recall-0.15": 0.987113,
-    "recall-0.25": 0.993557,
-    "recall-0.5": 0.993557,
+    "recall-0.15": 0.990979,
+    "recall-0.25": 0.996134,
+    "recall-0.5": 0.996134,
     "num_labels": 776
   },
   "elbow_right": {
     "count": 773,
-    "mean": 0.051632,
-    "median": 0.046512,
-    "std": 0.034104,
-    "sem": 0.001227,
-    "min": 0.007278,
-    "max": 0.454768,
-    "recall-0.025": 0.11583,
-    "recall-0.05": 0.566281,
+    "mean": 0.052458,
+    "median": 0.047666,
+    "std": 0.035088,
+    "sem": 0.001263,
+    "min": 0.003696,
+    "max": 0.494536,
+    "recall-0.025": 0.114543,
+    "recall-0.05": 0.541828,
     "recall-0.1": 0.94852,
-    "recall-0.15": 0.985843,
-    "recall-0.25": 0.988417,
+    "recall-0.15": 0.98713,
+    "recall-0.25": 0.990991,
     "recall-0.5": 0.994852,
     "num_labels": 777
   },
   "wrist_left": {
-    "count": 765,
-    "mean": 0.050433,
-    "median": 0.04224,
-    "std": 0.031081,
-    "sem": 0.001124,
-    "min": 0.005464,
-    "max": 0.287068,
-    "recall-0.025": 0.146753,
+    "count": 767,
+    "mean": 0.049785,
+    "median": 0.042228,
+    "std": 0.030582,
+    "sem": 0.001105,
+    "min": 0.004885,
+    "max": 0.255015,
+    "recall-0.025": 0.166234,
     "recall-0.05": 0.620779,
-    "recall-0.1": 0.932468,
-    "recall-0.15": 0.984416,
-    "recall-0.25": 0.98961,
-    "recall-0.5": 0.993506,
+    "recall-0.1": 0.935065,
+    "recall-0.15": 0.985714,
+    "recall-0.25": 0.994805,
+    "recall-0.5": 0.996104,
     "num_labels": 770
   },
   "wrist_right": {
     "count": 744,
-    "mean": 0.06004,
-    "median": 0.049851,
-    "std": 0.042609,
-    "sem": 0.001563,
-    "min": 0.005678,
-    "max": 0.36077,
-    "recall-0.025": 0.109479,
-    "recall-0.05": 0.502003,
+    "mean": 0.060522,
+    "median": 0.050427,
+    "std": 0.043931,
+    "sem": 0.001612,
+    "min": 0.008534,
+    "max": 0.488707,
+    "recall-0.025": 0.104139,
+    "recall-0.05": 0.488652,
     "recall-0.1": 0.871829,
-    "recall-0.15": 0.946595,
-    "recall-0.25": 0.985314,
+    "recall-0.15": 0.949266,
+    "recall-0.25": 0.987984,
     "recall-0.5": 0.993324,
     "num_labels": 749
   },
   "hip_left": {
-    "count": 776,
-    "mean": 0.063849,
-    "median": 0.0596,
-    "std": 0.026494,
-    "sem": 0.000952,
-    "min": 0.0017,
-    "max": 0.217641,
-    "recall-0.025": 0.026923,
-    "recall-0.05": 0.30641,
-    "recall-0.1": 0.919231,
-    "recall-0.15": 0.978205,
-    "recall-0.25": 0.994872,
-    "recall-0.5": 0.994872,
+    "count": 777,
+    "mean": 0.065059,
+    "median": 0.058995,
+    "std": 0.031143,
+    "sem": 0.001118,
+    "min": 0.006973,
+    "max": 0.333501,
+    "recall-0.025": 0.023077,
+    "recall-0.05": 0.333333,
+    "recall-0.1": 0.903846,
+    "recall-0.15": 0.971795,
+    "recall-0.25": 0.99359,
+    "recall-0.5": 0.996154,
     "num_labels": 780
   },
   "hip_right": {
-    "count": 776,
-    "mean": 0.051607,
-    "median": 0.044657,
-    "std": 0.028833,
-    "sem": 0.001036,
-    "min": 0.007308,
-    "max": 0.192378,
-    "recall-0.025": 0.111538,
-    "recall-0.05": 0.587179,
-    "recall-0.1": 0.934615,
+    "count": 777,
+    "mean": 0.05311,
+    "median": 0.045129,
+    "std": 0.03416,
+    "sem": 0.001226,
+    "min": 0.007262,
+    "max": 0.377279,
+    "recall-0.025": 0.114103,
+    "recall-0.05": 0.580769,
+    "recall-0.1": 0.917949,
     "recall-0.15": 0.979487,
-    "recall-0.25": 0.994872,
-    "recall-0.5": 0.994872,
+    "recall-0.25": 0.99359,
+    "recall-0.5": 0.996154,
     "num_labels": 780
   },
   "knee_left": {
-    "count": 775,
-    "mean": 0.045965,
-    "median": 0.043071,
-    "std": 0.02706,
-    "sem": 0.000973,
-    "min": 0.005523,
+    "count": 776,
+    "mean": 0.048619,
+    "median": 0.043976,
+    "std": 0.031522,
+    "sem": 0.001132,
+    "min": 0.006994,
     "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,
+    "recall-0.025": 0.174583,
+    "recall-0.05": 0.603338,
+    "recall-0.1": 0.961489,
+    "recall-0.15": 0.983312,
+    "recall-0.25": 0.992298,
+    "recall-0.5": 0.996149,
     "num_labels": 779
   },
   "knee_right": {
     "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,
+    "mean": 0.051105,
+    "median": 0.046587,
+    "std": 0.030419,
+    "sem": 0.001096,
+    "min": 0.008215,
+    "max": 0.420776,
+    "recall-0.025": 0.140827,
+    "recall-0.05": 0.560724,
+    "recall-0.1": 0.962532,
+    "recall-0.15": 0.984496,
+    "recall-0.25": 0.99354,
     "recall-0.5": 0.996124,
     "num_labels": 774
   },
   "ankle_left": {
-    "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,
+    "count": 775,
+    "mean": 0.042663,
+    "median": 0.037704,
+    "std": 0.032934,
+    "sem": 0.001184,
+    "min": 0.00377,
+    "max": 0.414303,
+    "recall-0.025": 0.240051,
+    "recall-0.05": 0.690629,
+    "recall-0.1": 0.97561,
+    "recall-0.15": 0.983312,
+    "recall-0.25": 0.98973,
+    "recall-0.5": 0.994865,
     "num_labels": 779
   },
   "ankle_right": {
-    "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,
+    "count": 768,
+    "mean": 0.045821,
+    "median": 0.040081,
+    "std": 0.033126,
+    "sem": 0.001196,
+    "min": 0.004582,
+    "max": 0.448386,
+    "recall-0.025": 0.212987,
+    "recall-0.05": 0.668831,
+    "recall-0.1": 0.954545,
+    "recall-0.15": 0.987013,
+    "recall-0.25": 0.994805,
+    "recall-0.5": 0.997403,
     "num_labels": 770
   },
   "joint_recalls": {
     "num_labels": 10047,
-    "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
+    "recall-0.025": 0.12163,
+    "recall-0.05": 0.53807,
+    "recall-0.1": 0.94118,
+    "recall-0.15": 0.98308,
+    "recall-0.25": 0.99273,
+    "recall-0.5": 0.99572
   }
 }
 {
   "total_parts": 10800,
-  "correct_parts": 10664,
-  "pcp": 0.987407
+  "correct_parts": 10645,
+  "pcp": 0.985648
 }
 ```
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index 3f674c3..071d718 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -352,13 +352,24 @@ def main():
         time_2d = time.time() - start
         print("2D time:", time_2d)
 
+        minscores = {
+            # Choose this depending on the fraction of invalid/missing persons
+            # A higher value reduces the number of proposals
+            "panoptic": 0.95,
+            "human36m": 0.96,
+            "mvor": 0.87,
+            "campus": 0.96,
+            "shelf": 0.96,
+        }
+        minscore = minscores.get(dataset_use, 0.95)
+
         start = time.time()
         if sum(np.sum(p) for p in poses_2d) == 0:
             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"], roomparams, joint_names_2d
+                poses_2d, label["cameras"], roomparams, joint_names_2d, minscore
             )
             poses2D = []
             for cam in label["cameras"]:
diff --git a/scripts/test_triangulate.py b/scripts/test_triangulate.py
index 7b9e215..f8ae332 100644
--- a/scripts/test_triangulate.py
+++ b/scripts/test_triangulate.py
@@ -288,17 +288,27 @@ def filter_poses(poses3D, poses2D, roomparams, joint_names, drop_few_limbs=True)
                 drop.append(i)
                 continue
 
-        # Drop persons with too small average limb length
+        # Drop persons with too small or high average limb length
         total_length = 0
+        total_limbs = 0
         for limb in main_limbs:
             start_idx = joint_names.index(limb[0])
             end_idx = joint_names.index(limb[1])
+            if pose[start_idx, -1] < 0.1 or pose[end_idx, -1] < 0.1:
+                continue
             limb_length = np.linalg.norm(pose[end_idx, :3] - pose[start_idx, :3])
             total_length += limb_length
-        average_length = total_length / len(main_limbs)
+            total_limbs += 1
+        if total_limbs == 0:
+            drop.append(i)
+            continue
+        average_length = total_length / total_limbs
         if average_length < 0.1:
             drop.append(i)
             continue
+        if average_length > 0.5:
+            drop.append(i)
+            continue
 
     new_poses3D = []
     new_poses2D = [[] for _ in range(len(poses2D))]
diff --git a/scripts/triangulate_poses.py b/scripts/triangulate_poses.py
index a627be2..4e18ab7 100644
--- a/scripts/triangulate_poses.py
+++ b/scripts/triangulate_poses.py
@@ -28,16 +28,17 @@ core_joints = [
 
 
 def undistort_points(points: np.ndarray, caminfo: dict):
-    K = np.array(caminfo["K"], dtype=np.float32)
-    DC = np.array(caminfo["DC"][0:5], dtype=np.float32)
-    w = caminfo["width"]
-    h = caminfo["height"]
+    """Undistorts 2D pixel coordinates"""
+
+    K = np.asarray(caminfo["K"], dtype=np.float32)
+    DC = np.asarray(caminfo["DC"][0:5], dtype=np.float32)
 
     # Undistort camera matrix
+    w = caminfo["width"]
+    h = caminfo["height"]
     newK, _ = cv2.getOptimalNewCameraMatrix(K, DC, (w, h), 1, (w, h))
-    caminfo = copy.deepcopy(caminfo)
-    caminfo["K"] = newK.tolist()
-    caminfo["DC"] = [0.0, 0.0, 0.0, 0.0, 0.0]
+    caminfo["K"] = newK
+    caminfo["DC"] = np.array([0.0, 0.0, 0.0, 0.0, 0.0])
 
     # Undistort points
     pshape = points.shape
@@ -45,7 +46,7 @@ def undistort_points(points: np.ndarray, caminfo: dict):
     points = cv2.undistortPoints(points, K, DC, P=newK)
     points = points.reshape(pshape)
 
-    return points, caminfo
+    return points
 
 
 # ==================================================================================================
@@ -54,10 +55,11 @@ def undistort_points(points: np.ndarray, caminfo: dict):
 def get_camera_P(cam):
     """Calculate opencv-style projection matrix"""
 
-    R = np.array(cam["R"])
-    T = np.array(cam["T"])
+    R = np.asarray(cam["R"])
+    T = np.asarray(cam["T"])
+    K = np.asarray(cam["K"])
     Tr = R @ (T * -1)
-    P = cam["K"] @ np.hstack([R, Tr])
+    P = K @ np.hstack([R, Tr])
     return P
 
 
@@ -69,8 +71,8 @@ def calc_pair_score(pair, poses_2d, camparams, roomparams, joint_names_2d, use_j
 
     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]])
+    pose1 = poses_2d[pair[0][0]][pair[0][2]]
+    pose2 = poses_2d[pair[0][1]][pair[0][3]]
 
     # Select core joints
     jids = [joint_names_2d.index(j) for j in use_joints]
@@ -88,12 +90,13 @@ 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
+    min_score = 0.1
+    mask1a = pose1[:, 2] >= min_score
+    mask2a = pose2[:, 2] >= min_score
     mask = mask1a & mask2a
 
-    # If no joints are visible return a low score
-    if np.sum(mask) == 0:
+    # If too few joints are visible return a low score
+    if np.sum(mask) < 3:
         pose3d = np.zeros([len(pose1), 4])
         score = 0.0
         return pose3d, score
@@ -104,8 +107,7 @@ def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
     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
+    points3d = (points3d / points3d[3, :])[0:3, :].T
     pose3d = np.zeros([len(pose1), 4])
     pose3d[mask] = np.concatenate([points3d, np.ones([points3d.shape[0], 1])], axis=-1)
 
@@ -115,8 +117,11 @@ def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
     maxs = np.max(pose3d[mask][:, 0:3], axis=0)
     rsize = np.array(roomparams["room_size"]) / 2
     rcent = np.array(roomparams["room_center"])
+    wdist = 0.1
     center_outside = np.any((mean > rsize + rcent) | (mean < -rsize + rcent))
-    limb_outside = np.any((maxs > rsize + rcent + 0.1) | (mins < -rsize + rcent - 0.1))
+    limb_outside = np.any(
+        (maxs > rsize + rcent + wdist) | (mins < -rsize + rcent - wdist)
+    )
     if center_outside or limb_outside:
         pose3d[:, 3] = 0.001
         score = 0.001
@@ -124,29 +129,33 @@ def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
 
     # Calculate reprojection error
     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]
-    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)
+    repro1, dists1 = utils_pose.project_poses(poses_3d, cam1, calc_dists=True)
+    repro2, dists2 = utils_pose.project_poses(poses_3d, cam2, calc_dists=True)
+    error1 = np.linalg.norm(pose1[mask, 0:2] - repro1[0, mask, 0:2], axis=1)
+    error2 = np.linalg.norm(pose2[mask, 0:2] - repro2[0, 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]
+    mask1b = (repro1[0, :, 2] < min_score)[mask]
+    mask2b = (repro2[0, :, 2] < min_score)[mask]
     error1[mask1b] = penalty
     error2[mask2b] = penalty
 
     # Convert errors to a score
-    scale = (cam1["width"] + cam1["height"]) / 2
-    error1 = error1.clip(0, scale / 4)
-    error2 = error2.clip(0, scale / 4)
-    error1 = error1 / scale
-    error2 = error2 / scale
+    # Scale by image size and distance to the camera
+    iscale = (cam1["width"] + cam1["height"]) / 2
+    error1 = error1.clip(0, iscale / 4) / iscale
+    error2 = error2.clip(0, iscale / 4) / iscale
+    dscale1 = np.sqrt(np.mean(dists1[0, mask]) / 3.5)
+    dscale2 = np.sqrt(np.mean(dists2[0, mask]) / 3.5)
+    error1 = error1 * dscale1
+    error2 = error2 * dscale2
     error = (error1 + error2) / 2
     scores = 1.0 / (1.0 + error * 10)
-    score = np.mean(scores)
+
+    # Drop lowest scores
+    drop_k = math.floor(len(pose1) * 0.2)
+    score = np.mean(np.sort(scores, axis=-1)[drop_k:])
 
     # Add score to 3D pose
     full_scores = np.zeros([poses_3d.shape[1], 1])
@@ -159,11 +168,10 @@ def calc_pose_scored(pose1, pose2, cam1, cam2, roomparams):
 # ==================================================================================================
 
 
-def calc_grouping(all_pairs):
+def calc_grouping(all_pairs, min_score: float):
     """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]
@@ -177,7 +185,7 @@ def calc_grouping(all_pairs):
 
         # Create new group if non exists
         if len(groups) == 0:
-            groups.append([pair[4], pair[2][0], [pair]])
+            groups.append([pair[3], pair[2][0], [pair]])
             continue
 
         # Check if the pair matches to an existing group
@@ -188,7 +196,7 @@ def calc_grouping(all_pairs):
         for j in range(len(groups)):
             g0 = groups[j]
             center0 = g0[0]
-            center1 = pair[4]
+            center1 = pair[3]
             if np.linalg.norm(center0 - center1) < max_center_dist:
                 pose0 = g0[1]
                 pose1 = pair[2][0]
@@ -206,14 +214,14 @@ def calc_grouping(all_pairs):
         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_center = (group[0] * len(group[1]) + pair[3]) / (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]])
+            groups.append([pair[3], pair[2][0], [pair]])
 
     return groups
 
@@ -221,26 +229,38 @@ def calc_grouping(all_pairs):
 # ==================================================================================================
 
 
-def merge_group(poses_3d: np.ndarray):
+def merge_group(poses_3d: np.ndarray, min_score: float):
     """Merges a group of poses into a single pose"""
 
     # 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)
+    imask = poses_3d[:, :, 3:4] > min_score
+    sum_poses = np.sum(poses_3d * imask, axis=0)
+    sum_mask = np.sum(imask, axis=0)
     initial_pose_3d = np.divide(
         sum_poses, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_poses)
     )
 
+    # Use center as default if the initial pose is empty
+    jmask = initial_pose_3d[:, 3] > 0.0
+    sum_joints = np.sum(initial_pose_3d[jmask, 0:3], axis=0)
+    sum_mask = np.sum(jmask)
+    center = np.divide(
+        sum_joints, sum_mask, where=(sum_mask > 0), out=np.zeros_like(sum_joints)
+    )
+    initial_pose_3d[~jmask, 0:3] = center
+
+    # Drop joints with low scores
+    offset = 0.1
+    mask = poses_3d[:, :, 3:4] > (min_score - offset)
+
     # Drop outliers that are far away from the other proposals
-    max_dist = 0.3
+    max_dist = 1.2
     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)
+    dmask = distances <= max_dist
+    mask = mask & np.expand_dims(dmask, axis=-1)
 
     # Select the best-k proposals for each joint that are closest to the initial pose
     keep_best = 3
@@ -265,13 +285,24 @@ def merge_group(poses_3d: np.ndarray):
 # ==================================================================================================
 
 
-def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d):
+def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d, min_score=0.95):
+    """Triangulates 3D poses from 2D poses of multiple views"""
+
+    # Convert poses and camparams to numpy arrays
+    camparams = copy.deepcopy(camparams)
+    for i in range(len(camparams)):
+        poses_2d[i] = np.asarray(poses_2d[i])
+        camparams[i]["K"] = np.array(camparams[i]["K"])
+        camparams[i]["R"] = np.array(camparams[i]["R"])
+        camparams[i]["T"] = np.array(camparams[i]["T"])
+        camparams[i]["DC"] = np.array(camparams[i]["DC"][0:5])
 
     # Undistort 2D points
-    for i, cam in enumerate(camparams):
-        poses = np.array(poses_2d[i])
-        poses[:, :, 0:2], camparams[i] = undistort_points(poses[:, :, 0:2], cam)
-        poses_2d[i] = poses.tolist()
+    for i in range(len(camparams)):
+        poses = poses_2d[i]
+        cam = camparams[i]
+        poses[:, :, 0:2] = undistort_points(poses[:, :, 0:2], cam)
+        poses_2d[i] = poses
 
     # Create pairs of persons
     num_persons = [len(p) for p in poses_2d]
@@ -302,28 +333,27 @@ def get_3d_pose(poses_2d, camparams, roomparams, joint_names_2d):
         # draw_utils.utils_view.show_plots()
 
     # Drop pairs with low scores
-    min_score = 0.9
     all_pairs = [p for p in all_pairs if p[2][1] > min_score]
 
+    # Group pairs that share a person
+    groups = calc_grouping(all_pairs, 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]])
+        pose1 = poses_2d[pair[0][0]][pair[0][2]]
+        pose2 = 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:
-        poses = np.array([p[3] for p in group[2]])
-        pose_3d = merge_group(poses)
+        poses = np.array([p[4] for p in group[2]])
+        pose_3d = merge_group(poses, min_score)
         poses_3d.append(pose_3d)
 
     if len(poses_3d) > 0:
diff --git a/skelda b/skelda
index dcf3a0c..85db8d3 160000
--- a/skelda
+++ b/skelda
@@ -1 +1 @@
-Subproject commit dcf3a0c3df75915b4ab002c085e52f0c566cfa23
+Subproject commit 85db8d366dea4bf772b74dd97fc7b90881f1ba1d