diff --git a/media/RESULTS.md b/media/RESULTS.md
index 632ed24..9d333f7 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -828,125 +828,125 @@ Results of the model in various experiments on different datasets.
   "person_nums": {
     "total_frames": 629,
     "total_labels": 1061,
-    "total_preds": 1000,
+    "total_preds": 998,
     "considered_empty": 0,
-    "valid_preds": 762,
-    "invalid_preds": 238,
-    "missing": 299,
-    "invalid_fraction": 0.238,
-    "precision": 0.762,
-    "recall": 0.71819,
-    "f1": 0.73945,
-    "non_empty": 1000
+    "valid_preds": 761,
+    "invalid_preds": 237,
+    "missing": 300,
+    "invalid_fraction": 0.23747,
+    "precision": 0.76253,
+    "recall": 0.71725,
+    "f1": 0.73919,
+    "non_empty": 998
   },
   "mpjpe": {
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-    "mean": 0.11579,
-    "median": 0.095056,
-    "std": 0.067568,
-    "sem": 0.002449,
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     "recall-0.05": 0.005655,
     "recall-0.1": 0.38737,
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-    "recall-0.5": 0.71819,
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     "num_labels": 1061,
     "ap-0.025": 0.0,
     "ap-0.05": 9.8e-05,
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-    "ap-0.15": 0.4902,
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+    "ap-0.1": 0.249132,
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   "head": {
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     "recall-0.05": 0.356268,
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     "recall-0.5": 0.716305,
     "num_labels": 1061
   },
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+    "num_labels": 1061
+  },
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     "num_labels": 1061
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   "elbow_left": {
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     "num_labels": 1061
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   "elbow_right": {
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     "num_labels": 1061
   },
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@@ -954,47 +954,47 @@ Results of the model in various experiments on different datasets.
     "recall-0.1": 0.380773,
     "recall-0.15": 0.473139,
     "recall-0.25": 0.614515,
-    "recall-0.5": 0.709708,
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     "num_labels": 1061
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   "wrist_right": {
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     "recall-0.1": 0.430726,
     "recall-0.15": 0.527804,
-    "recall-0.25": 0.622055,
-    "recall-0.5": 0.704995,
+    "recall-0.25": 0.621112,
+    "recall-0.5": 0.704053,
     "num_labels": 1061
   },
   "hip_left": {
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+    "median": 0.17065,
+    "std": 0.085602,
     "sem": 0.003138,
     "min": 0.021758,
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     "recall-0.025": 0.000943,
     "recall-0.05": 0.00377,
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-    "recall-0.15": 0.261074,
-    "recall-0.25": 0.575872,
-    "recall-0.5": 0.70311,
+    "recall-0.1": 0.065975,
+    "recall-0.15": 0.260132,
+    "recall-0.25": 0.574929,
+    "recall-0.5": 0.702168,
     "num_labels": 1061
   },
   "hip_right": {
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+    "median": 0.167368,
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+    "sem": 0.002823,
     "min": 0.036054,
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     "recall-0.025": 0.0,
@@ -1002,7 +1002,7 @@ Results of the model in various experiments on different datasets.
     "recall-0.1": 0.069746,
     "recall-0.15": 0.275212,
     "recall-0.25": 0.588124,
-    "recall-0.5": 0.698398,
+    "recall-0.5": 0.697455,
     "num_labels": 1061
   },
   "knee_left": {},
@@ -1013,16 +1013,16 @@ Results of the model in various experiments on different datasets.
     "num_labels": 9549,
     "recall-0.025": 0.07666,
     "recall-0.05": 0.24065,
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-    "recall-0.15": 0.52341,
-    "recall-0.25": 0.64572,
-    "recall-0.5": 0.70803
+    "recall-0.1": 0.41627,
+    "recall-0.15": 0.52278,
+    "recall-0.25": 0.64426,
+    "recall-0.5": 0.70699
   }
 }
 {
   "total_parts": 10610,
-  "correct_parts": 5846,
-  "pcp": 0.55099
+  "correct_parts": 5842,
+  "pcp": 0.550613
 }
 ```
 
@@ -1039,43 +1039,43 @@ Results of the model in various experiments on different datasets.
   "person_nums": {
     "total_frames": 420,
     "total_labels": 1466,
-    "total_preds": 1542,
+    "total_preds": 1540,
     "considered_empty": 0,
     "valid_preds": 1464,
-    "invalid_preds": 78,
+    "invalid_preds": 76,
     "missing": 2,
-    "invalid_fraction": 0.05058,
-    "precision": 0.94942,
+    "invalid_fraction": 0.04935,
+    "precision": 0.95065,
     "recall": 0.99864,
-    "f1": 0.9734,
-    "non_empty": 1542
+    "f1": 0.97405,
+    "non_empty": 1540
   },
   "mpjpe": {
     "count": 1464,
-    "mean": 0.042704,
-    "median": 0.037986,
-    "std": 0.020188,
+    "mean": 0.042718,
+    "median": 0.038,
+    "std": 0.020204,
     "sem": 0.000528,
     "min": 0.01189,
     "max": 0.147865,
     "recall-0.025": 0.153479,
-    "recall-0.05": 0.741473,
+    "recall-0.05": 0.742156,
     "recall-0.1": 0.982265,
     "recall-0.15": 0.998636,
     "recall-0.25": 0.998636,
     "recall-0.5": 0.998636,
     "num_labels": 1466,
-    "ap-0.025": 0.064044,
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-    "ap-0.1": 0.960955,
-    "ap-0.15": 0.98397,
-    "ap-0.25": 0.98397,
-    "ap-0.5": 0.98397
+    "ap-0.025": 0.064081,
+    "ap-0.05": 0.663593,
+    "ap-0.1": 0.961019,
+    "ap-0.15": 0.98405,
+    "ap-0.25": 0.98405,
+    "ap-0.5": 0.98405
   },
   "nose": {
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     "min": 0.001407,
@@ -1090,13 +1090,13 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_left": {
     "count": 1464,
-    "mean": 0.021383,
+    "mean": 0.021397,
     "median": 0.01901,
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     "min": 0.002677,
     "max": 0.095793,
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     "recall-0.05": 0.97749,
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     "recall-0.15": 0.998636,
@@ -1106,13 +1106,13 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 1463,
-    "mean": 0.022675,
-    "median": 0.019544,
-    "std": 0.014416,
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     "sem": 0.000377,
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+    "recall-0.025": 0.712628,
     "recall-0.05": 0.953584,
     "recall-0.1": 0.995904,
     "recall-0.15": 0.998635,
@@ -1122,13 +1122,13 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 1463,
-    "mean": 0.026263,
+    "mean": 0.026268,
     "median": 0.017747,
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     "sem": 0.000753,
     "min": 0.001259,
     "max": 0.323155,
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     "recall-0.05": 0.882594,
     "recall-0.1": 0.979522,
     "recall-0.15": 0.986348,
@@ -1138,14 +1138,14 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
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-    "mean": 0.023413,
+    "mean": 0.023411,
     "median": 0.01824,
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+    "std": 0.018218,
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     "min": 0.001407,
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+    "max": 0.227062,
     "recall-0.025": 0.687628,
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+    "recall-0.05": 0.926863,
     "recall-0.1": 0.993848,
     "recall-0.15": 0.997949,
     "recall-0.25": 0.999316,
@@ -1154,15 +1154,15 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_left": {
     "count": 1429,
-    "mean": 0.03518,
+    "mean": 0.035199,
     "median": 0.01696,
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+    "std": 0.050725,
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     "max": 0.397255,
     "recall-0.025": 0.675732,
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     "recall-0.15": 0.95537,
     "recall-0.25": 0.98675,
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@@ -1170,30 +1170,30 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
     "count": 1456,
-    "mean": 0.024194,
-    "median": 0.016335,
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+    "sem": 0.00065,
     "min": 0.001905,
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+    "recall-0.25": 0.999313,
     "recall-0.5": 1.0,
     "num_labels": 1456
   },
   "hip_left": {
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-    "mean": 0.05939,
-    "median": 0.053492,
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+    "mean": 0.059396,
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     "min": 0.005563,
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     "recall-0.25": 0.998635,
@@ -1202,9 +1202,9 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 1464,
-    "mean": 0.058564,
+    "mean": 0.058562,
     "median": 0.054479,
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+    "std": 0.028875,
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     "min": 0.003776,
     "max": 0.304525,
@@ -1218,30 +1218,30 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
     "count": 1463,
-    "mean": 0.051818,
+    "mean": 0.051829,
     "median": 0.042757,
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+    "std": 0.042202,
+    "sem": 0.001104,
     "min": 0.001938,
     "max": 0.3408,
-    "recall-0.025": 0.187713,
-    "recall-0.05": 0.63413,
+    "recall-0.025": 0.188396,
+    "recall-0.05": 0.633447,
     "recall-0.1": 0.927645,
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+    "recall-0.15": 0.967235,
     "recall-0.25": 0.984983,
     "recall-0.5": 0.998635,
     "num_labels": 1465
   },
   "knee_right": {
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-    "mean": 0.048414,
+    "mean": 0.048415,
     "median": 0.042583,
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+    "std": 0.029674,
     "sem": 0.000778,
     "min": 0.003543,
     "max": 0.324497,
-    "recall-0.025": 0.213845,
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     "recall-0.1": 0.938314,
     "recall-0.15": 0.992461,
     "recall-0.25": 0.996573,
@@ -1250,14 +1250,14 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_left": {
     "count": 1456,
-    "mean": 0.083238,
-    "median": 0.037284,
-    "std": 0.106307,
+    "mean": 0.083278,
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+    "std": 0.106297,
     "sem": 0.002787,
     "min": 0.002453,
     "max": 0.495774,
     "recall-0.025": 0.362953,
-    "recall-0.05": 0.588517,
+    "recall-0.05": 0.587833,
     "recall-0.1": 0.732057,
     "recall-0.15": 0.821599,
     "recall-0.25": 0.913876,
@@ -1266,14 +1266,14 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
     "count": 1449,
-    "mean": 0.077886,
+    "mean": 0.077908,
     "median": 0.033398,
-    "std": 0.103863,
+    "std": 0.103855,
     "sem": 0.002729,
     "min": 0.001061,
     "max": 0.4981,
     "recall-0.025": 0.369863,
-    "recall-0.05": 0.684932,
+    "recall-0.05": 0.684247,
     "recall-0.1": 0.772603,
     "recall-0.15": 0.813699,
     "recall-0.25": 0.896575,
@@ -1282,18 +1282,18 @@ Results of the model in various experiments on different datasets.
   },
   "joint_recalls": {
     "num_labels": 18990,
-    "recall-0.025": 0.48752,
-    "recall-0.05": 0.75055,
+    "recall-0.025": 0.48773,
+    "recall-0.05": 0.75039,
     "recall-0.1": 0.92507,
     "recall-0.15": 0.95956,
-    "recall-0.25": 0.98167,
+    "recall-0.25": 0.98157,
     "recall-0.5": 0.99768
   }
 }
 {
   "total_parts": 20444,
-  "correct_parts": 19980,
-  "pcp": 0.977304
+  "correct_parts": 19978,
+  "pcp": 0.977206
 }
 ```
 
@@ -1577,49 +1577,49 @@ Results of the model in various experiments on different datasets.
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@@ -1740,14 +1740,14 @@ Results of the model in various experiments on different datasets.
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@@ -1756,76 +1756,76 @@ Results of the model in various experiments on different datasets.
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@@ -1915,13 +1915,13 @@ Results of the model in various experiments on different datasets.
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@@ -1979,90 +1979,90 @@ Results of the model in various experiments on different datasets.
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+    "std": 0.060778,
+    "sem": 0.004255,
     "min": 0.030718,
     "max": 0.497295,
     "recall-0.025": 0.0,
     "recall-0.05": 0.030769,
-    "recall-0.1": 0.515385,
+    "recall-0.1": 0.511538,
     "recall-0.15": 0.707692,
     "recall-0.25": 0.765385,
     "recall-0.5": 0.788462,
     "num_labels": 260
   },
   "knee_left": {
-    "count": 215,
-    "mean": 0.14778,
-    "median": 0.112225,
-    "std": 0.104573,
-    "sem": 0.007148,
+    "count": 214,
+    "mean": 0.149731,
+    "median": 0.111341,
+    "std": 0.109357,
+    "sem": 0.007493,
     "min": 0.010824,
     "max": 0.491249,
     "recall-0.025": 0.028986,
-    "recall-0.05": 0.101449,
-    "recall-0.1": 0.326087,
+    "recall-0.05": 0.105072,
+    "recall-0.1": 0.32971,
     "recall-0.15": 0.503623,
-    "recall-0.25": 0.637681,
-    "recall-0.5": 0.778986,
+    "recall-0.25": 0.630435,
+    "recall-0.5": 0.775362,
     "num_labels": 276
   },
   "knee_right": {
     "count": 164,
-    "mean": 0.14477,
-    "median": 0.099527,
-    "std": 0.116266,
-    "sem": 0.009107,
+    "mean": 0.145171,
+    "median": 0.098903,
+    "std": 0.117423,
+    "sem": 0.009197,
     "min": 0.015332,
-    "max": 0.497051,
+    "max": 0.489112,
     "recall-0.025": 0.018605,
-    "recall-0.05": 0.130233,
-    "recall-0.1": 0.381395,
-    "recall-0.15": 0.525581,
+    "recall-0.05": 0.134884,
+    "recall-0.1": 0.386047,
+    "recall-0.15": 0.52093,
     "recall-0.25": 0.627907,
     "recall-0.5": 0.762791,
     "num_labels": 215
   },
   "ankle_left": {
-    "count": 54,
-    "mean": 0.341155,
-    "median": 0.378166,
-    "std": 0.111535,
-    "sem": 0.01532,
+    "count": 55,
+    "mean": 0.341998,
+    "median": 0.379434,
+    "std": 0.110689,
+    "sem": 0.015063,
     "min": 0.064544,
     "max": 0.490279,
     "recall-0.025": 0.0,
@@ -2070,17 +2070,17 @@ Results of the model in various experiments on different datasets.
     "recall-0.1": 0.022727,
     "recall-0.15": 0.030303,
     "recall-0.25": 0.083333,
-    "recall-0.5": 0.409091,
+    "recall-0.5": 0.416667,
     "num_labels": 132
   },
   "ankle_right": {
     "count": 56,
-    "mean": 0.366611,
+    "mean": 0.365706,
     "median": 0.380989,
-    "std": 0.099154,
-    "sem": 0.01337,
+    "std": 0.098209,
+    "sem": 0.013242,
     "min": 0.113098,
-    "max": 0.494649,
+    "max": 0.491617,
     "recall-0.025": 0.0,
     "recall-0.05": 0.0,
     "recall-0.1": 0.0,
@@ -2091,18 +2091,18 @@ Results of the model in various experiments on different datasets.
   },
   "joint_recalls": {
     "num_labels": 4457,
-    "recall-0.025": 0.26318,
-    "recall-0.05": 0.49809,
-    "recall-0.1": 0.65448,
-    "recall-0.15": 0.71618,
-    "recall-0.25": 0.7505,
-    "recall-0.5": 0.80054
+    "recall-0.025": 0.25712,
+    "recall-0.05": 0.49652,
+    "recall-0.1": 0.65492,
+    "recall-0.15": 0.71595,
+    "recall-0.25": 0.75028,
+    "recall-0.5": 0.80031
   }
 }
 {
   "total_parts": 4313,
-  "correct_parts": 3237,
-  "pcp": 0.750522
+  "correct_parts": 3239,
+  "pcp": 0.750985
 }
 ```
 
@@ -2132,31 +2132,31 @@ Results of the model in various experiments on different datasets.
   },
   "mpjpe": {
     "count": 630,
-    "mean": 0.056111,
+    "mean": 0.056103,
     "median": 0.051456,
-    "std": 0.018368,
-    "sem": 0.000732,
+    "std": 0.018372,
+    "sem": 0.000733,
     "min": 0.028965,
     "max": 0.14306,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.438095,
+    "recall-0.05": 0.439683,
     "recall-0.1": 0.949206,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 630,
     "ap-0.025": 0.0,
-    "ap-0.05": 0.223683,
-    "ap-0.1": 0.928267,
-    "ap-0.15": 0.999816,
-    "ap-0.25": 0.999816,
-    "ap-0.5": 0.999816
+    "ap-0.05": 0.224824,
+    "ap-0.1": 0.928154,
+    "ap-0.15": 0.999746,
+    "ap-0.25": 0.999746,
+    "ap-0.5": 0.999746
   },
   "head": {
     "count": 598,
-    "mean": 0.040764,
+    "mean": 0.040759,
     "median": 0.039496,
-    "std": 0.01374,
+    "std": 0.013741,
     "sem": 0.000562,
     "min": 0.011364,
     "max": 0.102955,
@@ -2170,10 +2170,10 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_left": {
     "count": 630,
-    "mean": 0.062839,
+    "mean": 0.062835,
     "median": 0.060457,
-    "std": 0.019404,
-    "sem": 0.000774,
+    "std": 0.019397,
+    "sem": 0.000773,
     "min": 0.018922,
     "max": 0.132634,
     "recall-0.025": 0.003175,
@@ -2186,14 +2186,14 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 630,
-    "mean": 0.066282,
+    "mean": 0.066268,
     "median": 0.06401,
-    "std": 0.019871,
-    "sem": 0.000792,
+    "std": 0.01988,
+    "sem": 0.000793,
     "min": 0.0267,
     "max": 0.146825,
     "recall-0.025": 0.0,
-    "recall-0.05": 0.250794,
+    "recall-0.05": 0.252381,
     "recall-0.1": 0.934921,
     "recall-0.15": 1.0,
     "recall-0.25": 1.0,
@@ -2202,9 +2202,9 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 630,
-    "mean": 0.05237,
+    "mean": 0.052366,
     "median": 0.049508,
-    "std": 0.020206,
+    "std": 0.02021,
     "sem": 0.000806,
     "min": 0.010131,
     "max": 0.140634,
@@ -2218,9 +2218,9 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
     "count": 629,
-    "mean": 0.055812,
+    "mean": 0.055804,
     "median": 0.048387,
-    "std": 0.03206,
+    "std": 0.032053,
     "sem": 0.001279,
     "min": 0.004074,
     "max": 0.228126,
@@ -2234,9 +2234,9 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_left": {
     "count": 630,
-    "mean": 0.048071,
-    "median": 0.041989,
-    "std": 0.026751,
+    "mean": 0.048061,
+    "median": 0.041971,
+    "std": 0.026754,
     "sem": 0.001067,
     "min": 0.007895,
     "max": 0.191578,
@@ -2250,10 +2250,10 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
     "count": 625,
-    "mean": 0.052705,
+    "mean": 0.052694,
     "median": 0.047416,
-    "std": 0.025887,
-    "sem": 0.001036,
+    "std": 0.0259,
+    "sem": 0.001037,
     "min": 0.008634,
     "max": 0.226556,
     "recall-0.025": 0.0816,
@@ -2266,9 +2266,9 @@ Results of the model in various experiments on different datasets.
   },
   "hip_left": {
     "count": 630,
-    "mean": 0.057311,
-    "median": 0.054171,
-    "std": 0.020577,
+    "mean": 0.057289,
+    "median": 0.054137,
+    "std": 0.020575,
     "sem": 0.00082,
     "min": 0.014001,
     "max": 0.17071,
@@ -2282,7 +2282,7 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
     "count": 629,
-    "mean": 0.055245,
+    "mean": 0.055246,
     "median": 0.050996,
     "std": 0.023089,
     "sem": 0.000921,
@@ -2298,14 +2298,14 @@ Results of the model in various experiments on different datasets.
   },
   "knee_left": {
     "count": 628,
-    "mean": 0.045693,
+    "mean": 0.045669,
     "median": 0.034743,
-    "std": 0.046075,
+    "std": 0.046074,
     "sem": 0.00184,
     "min": 0.003593,
     "max": 0.364064,
     "recall-0.025": 0.291401,
-    "recall-0.05": 0.72293,
+    "recall-0.05": 0.724522,
     "recall-0.1": 0.941083,
     "recall-0.15": 0.961783,
     "recall-0.25": 0.984076,
@@ -2330,7 +2330,7 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_left": {
     "count": 619,
-    "mean": 0.065864,
+    "mean": 0.065863,
     "median": 0.050491,
     "std": 0.072779,
     "sem": 0.002928,
@@ -2346,13 +2346,13 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
     "count": 601,
-    "mean": 0.054043,
+    "mean": 0.054029,
     "median": 0.047411,
-    "std": 0.042559,
-    "sem": 0.001737,
+    "std": 0.042569,
+    "sem": 0.001738,
     "min": 0.011391,
     "max": 0.47977,
-    "recall-0.025": 0.094855,
+    "recall-0.025": 0.096463,
     "recall-0.05": 0.522508,
     "recall-0.1": 0.92926,
     "recall-0.15": 0.946945,
@@ -2362,8 +2362,8 @@ Results of the model in various experiments on different datasets.
   },
   "joint_recalls": {
     "num_labels": 8129,
-    "recall-0.025": 0.09583,
-    "recall-0.05": 0.52737,
+    "recall-0.025": 0.09595,
+    "recall-0.05": 0.52749,
     "recall-0.1": 0.95092,
     "recall-0.15": 0.98155,
     "recall-0.25": 0.98905,
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index 6ef5b13..93affd3 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -236,7 +236,7 @@ def main():
     }
     min_group_size = min_group_sizes.get(dataset_use, 1)
     if dataset_use == "panoptic" and len(datasets["panoptic"]["cams"]) == 10:
-        min_group_size = 5
+        min_group_size = 4
 
     print("\nRunning predictions ...")
     all_poses = []
diff --git a/spt/triangulator.cpp b/spt/triangulator.cpp
index 98be43f..ac2b585 100644
--- a/spt/triangulator.cpp
+++ b/spt/triangulator.cpp
@@ -455,7 +455,7 @@ std::vector<std::vector<std::array<float, 4>>> TriangulatorInternal::triangulate
     }
 
     // Group pairs that share a person
-    std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> groups;
+    std::vector<std::tuple<cv::Point3f, cv::Mat, std::vector<int>>> groups;
     groups = calc_grouping(all_pairs, all_scored_poses, min_score);
 
     // Drop groups with too few matches
@@ -1149,7 +1149,7 @@ std::pair<cv::Mat, float> TriangulatorInternal::triangulate_and_score(
 
 // =================================================================================================
 
-std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInternal::calc_grouping(
+std::vector<std::tuple<cv::Point3f, cv::Mat, std::vector<int>>> TriangulatorInternal::calc_grouping(
     const std::vector<std::pair<std::tuple<int, int, int, int>, std::pair<int, int>>> &all_pairs,
     const std::vector<std::pair<cv::Mat, float>> &all_scored_poses,
     float min_score)
@@ -1159,14 +1159,14 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
     size_t num_pairs = all_pairs.size();
 
     // Calculate pose centers
-    std::vector<cv::Point3d> centers;
+    std::vector<cv::Point3f> centers;
     centers.resize(num_pairs);
     for (size_t i = 0; i < num_pairs; ++i)
     {
         const cv::Mat &pose_3d = all_scored_poses[i].first;
         size_t num_joints = pose_3d.rows;
 
-        cv::Point3d center(0, 0, 0);
+        cv::Point3f center(0, 0, 0);
         size_t num_valid = 0;
         for (size_t j = 0; j < num_joints; ++j)
         {
@@ -1193,20 +1193,21 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
 
     // Calculate Groups
     // defined as a tuple of center, pose, and all-pairs-indices of members
-    std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> groups;
+    std::vector<std::tuple<cv::Point3f, cv::Mat, std::vector<int>>> groups;
+    std::vector<std::vector<size_t>> per_group_visible_counts;
     for (size_t i = 0; i < num_pairs; ++i)
     {
         const cv::Mat &pose_3d = all_scored_poses[i].first;
         size_t num_joints = pose_3d.rows;
 
-        const cv::Point3d &center = centers[i];
+        const cv::Point3f &center = centers[i];
         float best_dist = std::numeric_limits<float>::infinity();
         int best_group = -1;
 
         for (size_t j = 0; j < groups.size(); ++j)
         {
             auto &group = groups[j];
-            cv::Point3d &group_center = std::get<0>(group);
+            cv::Point3f &group_center = std::get<0>(group);
 
             // Check if the center is close enough
             float dx = group_center.x - center.x;
@@ -1258,12 +1259,24 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
             // Create a new group
             std::vector<int> new_indices{static_cast<int>(i)};
             groups.emplace_back(center, pose_3d.clone(), std::move(new_indices));
+
+            // Update per joint counts
+            std::vector<size_t> new_valid_joint_counts(num_joints, 0);
+            for (size_t row = 0; row < num_joints; ++row)
+            {
+                const float *pose_3d_ptr = pose_3d.ptr<float>(row);
+                if (pose_3d_ptr[3] > min_score)
+                {
+                    new_valid_joint_counts[row] = 1;
+                }
+            }
+            per_group_visible_counts.push_back(std::move(new_valid_joint_counts));
         }
         else
         {
             // Update existing group
             auto &group = groups[best_group];
-            cv::Point3d &group_center = std::get<0>(group);
+            cv::Point3f &group_center = std::get<0>(group);
             cv::Mat &group_pose = std::get<1>(group);
             std::vector<int> &group_indices = std::get<2>(group);
 
@@ -1281,10 +1294,17 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
                 const float *pose_3d_ptr = pose_3d.ptr<float>(row);
                 float *group_pose_ptr = group_pose.ptr<float>(row);
 
-                group_pose_ptr[0] = (group_pose_ptr[0] * n_elems + pose_3d_ptr[0]) * inv_n1;
-                group_pose_ptr[1] = (group_pose_ptr[1] * n_elems + pose_3d_ptr[1]) * inv_n1;
-                group_pose_ptr[2] = (group_pose_ptr[2] * n_elems + pose_3d_ptr[2]) * inv_n1;
-                group_pose_ptr[3] = (group_pose_ptr[3] * n_elems + pose_3d_ptr[3]) * inv_n1;
+                if (pose_3d_ptr[3] > min_score)
+                {
+                    float j_elems = static_cast<float>(per_group_visible_counts[best_group][row]);
+                    float inv_j1 = 1.0 / (j_elems + 1.0);
+
+                    group_pose_ptr[0] = (group_pose_ptr[0] * j_elems + pose_3d_ptr[0]) * inv_j1;
+                    group_pose_ptr[1] = (group_pose_ptr[1] * j_elems + pose_3d_ptr[1]) * inv_j1;
+                    group_pose_ptr[2] = (group_pose_ptr[2] * j_elems + pose_3d_ptr[2]) * inv_j1;
+                    group_pose_ptr[3] = (group_pose_ptr[3] * j_elems + pose_3d_ptr[3]) * inv_j1;
+                    per_group_visible_counts[best_group][row]++;
+                }
             }
             group_indices.push_back(static_cast<int>(i));
         }
@@ -1342,7 +1362,7 @@ cv::Mat TriangulatorInternal::merge_group(const std::vector<cv::Mat> &poses_3d,
 
     // Use center as default if the initial pose is empty
     std::vector<u_char> jmask(num_joints, 0);
-    cv::Point3d center(0, 0, 0);
+    cv::Point3f center(0, 0, 0);
     int valid_joints = 0;
     for (int j = 0; j < num_joints; ++j)
     {
diff --git a/spt/triangulator.hpp b/spt/triangulator.hpp
index f569da8..2d8fe90 100644
--- a/spt/triangulator.hpp
+++ b/spt/triangulator.hpp
@@ -98,7 +98,7 @@ private:
         const std::array<std::array<float, 3>, 2> &roomparams,
         const std::vector<std::array<size_t, 2>> &core_limbs_idx);
 
-    std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> calc_grouping(
+    std::vector<std::tuple<cv::Point3f, cv::Mat, std::vector<int>>> calc_grouping(
         const std::vector<std::pair<std::tuple<int, int, int, int>, std::pair<int, int>>> &all_pairs,
         const std::vector<std::pair<cv::Mat, float>> &all_scored_poses,
         float min_score);