diff --git a/media/RESULTS.md b/media/RESULTS.md
index 0e78bac..275d34f 100644
--- a/media/RESULTS.md
+++ b/media/RESULTS.md
@@ -7,9 +7,9 @@ Results of the model in various experiments on different datasets.
 (duration 00:01:20)
 ```json
 {
-  "avg_time_2d": 0.07047960596569514,
-  "avg_time_3d": 0.0013772115869037176,
-  "avg_fps": 13.916563995726705
+  "avg_time_2d": 0.07087800987696244,
+  "avg_time_3d": 0.00041960861723301776,
+  "avg_fps": 14.025713917519038
 }
 {
   "person_nums": {
@@ -28,8 +28,8 @@ Results of the model in various experiments on different datasets.
   },
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-    "mean": 0.067041,
-    "median": 0.059381,
+    "mean": 0.067044,
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     "sem": 0.001135,
     "min": 0.043707,
@@ -43,36 +43,36 @@ Results of the model in various experiments on different datasets.
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     "ap-0.025": 0.0,
     "ap-0.05": 0.002029,
-    "ap-0.1": 0.893791,
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+    "ap-0.15": 0.91673,
     "ap-0.25": 1.0,
     "ap-0.5": 1.0
   },
   "nose": {
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-    "mean": 0.117446,
+    "mean": 0.117677,
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-    "min": 0.046467,
+    "std": 0.042982,
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     "max": 0.271134,
     "recall-0.025": 0.0,
     "recall-0.05": 0.003333,
     "recall-0.1": 0.485,
-    "recall-0.15": 0.808333,
+    "recall-0.15": 0.805,
     "recall-0.25": 0.995,
     "recall-0.5": 1.0,
     "num_labels": 600
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   "shoulder_left": {
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     "min": 0.003037,
     "max": 0.181945,
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     "recall-0.15": 0.965,
@@ -87,7 +87,7 @@ Results of the model in various experiments on different datasets.
     "std": 0.042119,
     "sem": 0.001721,
     "min": 0.003882,
-    "max": 0.247363,
+    "max": 0.247362,
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@@ -99,8 +99,8 @@ Results of the model in various experiments on different datasets.
   "elbow_left": {
     "count": 600,
     "mean": 0.042931,
-    "median": 0.034598,
-    "std": 0.034814,
+    "median": 0.034599,
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@@ -115,7 +115,7 @@ Results of the model in various experiments on different datasets.
   "elbow_right": {
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+    "median": 0.03334,
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     "min": 0.00667,
@@ -130,13 +130,13 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_left": {
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-    "recall-0.025": 0.511667,
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@@ -146,9 +146,9 @@ Results of the model in various experiments on different datasets.
   },
   "wrist_right": {
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@@ -178,15 +178,15 @@ Results of the model in various experiments on different datasets.
   },
   "hip_right": {
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@@ -211,7 +211,7 @@ Results of the model in various experiments on different datasets.
   "knee_right": {
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@@ -227,7 +227,7 @@ Results of the model in various experiments on different datasets.
   "ankle_left": {
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@@ -242,34 +242,34 @@ Results of the model in various experiments on different datasets.
   },
   "ankle_right": {
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   "joint_recalls": {
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+    "recall-0.15": 0.92962,
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     "recall-0.5": 0.99974
   }
 }
 {
   "total_parts": 8400,
-  "correct_parts": 8090,
-  "pcp": 0.963095
+  "correct_parts": 8089,
+  "pcp": 0.962976
 }
 ```
 
@@ -286,16 +286,16 @@ Results of the model in various experiments on different datasets.
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+    "non_empty": 887
   },
   "mpjpe": {
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@@ -549,16 +549,16 @@ Results of the model in various experiments on different datasets.
 (duration 00:00:29)
 ```json
 {
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-  "avg_time_3d": 0.0016649657825254045,
-  "avg_fps": 16.535950860955353
+  "avg_time_2d": 0.05682281952983928,
+  "avg_time_3d": 0.00042041292730367406,
+  "avg_fps": 17.46931396211217
 }
 {
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     "total_labels": 376,
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@@ -574,7 +574,7 @@ Results of the model in various experiments on different datasets.
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@@ -593,11 +593,11 @@ Results of the model in various experiments on different datasets.
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@@ -609,11 +609,11 @@ Results of the model in various experiments on different datasets.
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@@ -629,7 +629,7 @@ Results of the model in various experiments on different datasets.
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@@ -661,7 +661,7 @@ Results of the model in various experiments on different datasets.
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@@ -672,7 +672,7 @@ Results of the model in various experiments on different datasets.
   },
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@@ -693,7 +693,7 @@ Results of the model in various experiments on different datasets.
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@@ -709,7 +709,7 @@ Results of the model in various experiments on different datasets.
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@@ -768,8 +768,8 @@ Results of the model in various experiments on different datasets.
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@@ -788,7 +788,7 @@ Results of the model in various experiments on different datasets.
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@@ -820,188 +820,188 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:31)
 ```json
 {
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+  "avg_fps": 18.04581185125563
 }
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-    "recall-0.5": 0.70311,
+    "recall-0.025": 0.072573,
+    "recall-0.05": 0.240339,
+    "recall-0.1": 0.430726,
+    "recall-0.15": 0.527804,
+    "recall-0.25": 0.622055,
+    "recall-0.5": 0.704995,
     "num_labels": 1061
   },
   "hip_left": {
-    "count": 742,
-    "mean": 0.188889,
+    "count": 746,
+    "mean": 0.189195,
     "median": 0.17064,
-    "std": 0.084308,
-    "sem": 0.003097,
+    "std": 0.085658,
+    "sem": 0.003138,
     "min": 0.021758,
     "max": 0.499358,
     "recall-0.025": 0.000943,
     "recall-0.05": 0.00377,
-    "recall-0.1": 0.065033,
-    "recall-0.15": 0.259189,
-    "recall-0.25": 0.572102,
-    "recall-0.5": 0.69934,
+    "recall-0.1": 0.066918,
+    "recall-0.15": 0.261074,
+    "recall-0.25": 0.575872,
+    "recall-0.5": 0.70311,
     "num_labels": 1061
   },
   "hip_right": {
     "count": 741,
-    "mean": 0.182767,
-    "median": 0.167996,
-    "std": 0.079854,
-    "sem": 0.002935,
+    "mean": 0.180798,
+    "median": 0.167552,
+    "std": 0.076766,
+    "sem": 0.002822,
     "min": 0.036054,
-    "max": 0.492847,
+    "max": 0.492444,
     "recall-0.025": 0.0,
     "recall-0.05": 0.00377,
     "recall-0.1": 0.069746,
-    "recall-0.15": 0.27427,
-    "recall-0.25": 0.583412,
+    "recall-0.15": 0.275212,
+    "recall-0.25": 0.588124,
     "recall-0.5": 0.698398,
     "num_labels": 1061
   },
@@ -1011,18 +1011,18 @@ Results of the model in various experiments on different datasets.
   "ankle_right": {},
   "joint_recalls": {
     "num_labels": 9549,
-    "recall-0.025": 0.0753,
-    "recall-0.05": 0.24003,
-    "recall-0.1": 0.41481,
-    "recall-0.15": 0.52152,
-    "recall-0.25": 0.64321,
-    "recall-0.5": 0.70636
+    "recall-0.025": 0.07666,
+    "recall-0.05": 0.24065,
+    "recall-0.1": 0.41638,
+    "recall-0.15": 0.52341,
+    "recall-0.25": 0.64572,
+    "recall-0.5": 0.70803
   }
 }
 {
   "total_parts": 10610,
-  "correct_parts": 5825,
-  "pcp": 0.54901
+  "correct_parts": 5846,
+  "pcp": 0.55099
 }
 ```
 
@@ -1031,56 +1031,56 @@ Results of the model in various experiments on different datasets.
 (duration 00:02:28)
 ```json
 {
-  "avg_time_2d": 0.10984443804112877,
-  "avg_time_3d": 0.004945937598623881,
-  "avg_fps": 8.71153173275002
+  "avg_time_2d": 0.11023792871614782,
+  "avg_time_3d": 0.0007589270428913395,
+  "avg_fps": 9.00926420988789
 }
 {
   "person_nums": {
     "total_frames": 420,
     "total_labels": 1466,
-    "total_preds": 1549,
-    "considered_empty": 7,
+    "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": 0.042745,
-    "median": 0.037965,
-    "std": 0.020222,
-    "sem": 0.000529,
+    "mean": 0.042705,
+    "median": 0.037986,
+    "std": 0.020188,
+    "sem": 0.000528,
     "min": 0.01189,
     "max": 0.147865,
     "recall-0.025": 0.153479,
-    "recall-0.05": 0.742156,
+    "recall-0.05": 0.741473,
     "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": 0.064846,
-    "ap-0.05": 0.664636,
-    "ap-0.1": 0.96085,
-    "ap-0.15": 0.983955,
-    "ap-0.25": 0.983955,
-    "ap-0.5": 0.983955
+    "ap-0.025": 0.064039,
+    "ap-0.05": 0.663091,
+    "ap-0.1": 0.960957,
+    "ap-0.15": 0.983972,
+    "ap-0.25": 0.983972,
+    "ap-0.5": 0.983972
   },
   "nose": {
     "count": 1462,
-    "mean": 0.016118,
+    "mean": 0.016084,
     "median": 0.012438,
-    "std": 0.019038,
-    "sem": 0.000498,
+    "std": 0.019001,
+    "sem": 0.000497,
     "min": 0.001407,
     "max": 0.282177,
-    "recall-0.025": 0.900889,
+    "recall-0.025": 0.901572,
     "recall-0.05": 0.96514,
     "recall-0.1": 0.995215,
     "recall-0.15": 0.995899,
@@ -1090,13 +1090,13 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_left": {
     "count": 1464,
-    "mean": 0.021387,
-    "median": 0.019035,
-    "std": 0.011197,
+    "mean": 0.021383,
+    "median": 0.01901,
+    "std": 0.011213,
     "sem": 0.000293,
     "min": 0.002677,
-    "max": 0.095794,
-    "recall-0.025": 0.705321,
+    "max": 0.095793,
+    "recall-0.025": 0.706685,
     "recall-0.05": 0.97749,
     "recall-0.1": 0.998636,
     "recall-0.15": 0.998636,
@@ -1106,13 +1106,13 @@ Results of the model in various experiments on different datasets.
   },
   "shoulder_right": {
     "count": 1463,
-    "mean": 0.022631,
-    "median": 0.019541,
-    "std": 0.014416,
+    "mean": 0.022677,
+    "median": 0.019544,
+    "std": 0.014417,
     "sem": 0.000377,
     "min": 0.00112,
     "max": 0.146597,
-    "recall-0.025": 0.714676,
+    "recall-0.025": 0.711945,
     "recall-0.05": 0.953584,
     "recall-0.1": 0.995904,
     "recall-0.15": 0.998635,
@@ -1122,15 +1122,15 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_left": {
     "count": 1463,
-    "mean": 0.02639,
-    "median": 0.017858,
-    "std": 0.02901,
-    "sem": 0.000759,
+    "mean": 0.026286,
+    "median": 0.017747,
+    "std": 0.028804,
+    "sem": 0.000753,
     "min": 0.001259,
     "max": 0.323155,
     "recall-0.025": 0.704437,
-    "recall-0.05": 0.882594,
-    "recall-0.1": 0.978157,
+    "recall-0.05": 0.881911,
+    "recall-0.1": 0.979522,
     "recall-0.15": 0.986348,
     "recall-0.25": 0.996587,
     "recall-0.5": 0.998635,
@@ -1138,8 +1138,8 @@ Results of the model in various experiments on different datasets.
   },
   "elbow_right": {
     "count": 1462,
-    "mean": 0.023401,
-    "median": 0.018196,
+    "mean": 0.02341,
+    "median": 0.01824,
     "std": 0.018141,
     "sem": 0.000475,
     "min": 0.001407,
@@ -1153,147 +1153,147 @@ Results of the model in various experiments on different datasets.
     "num_labels": 1463
   },
   "wrist_left": {
-    "count": 1430,
-    "mean": 0.035377,
-    "median": 0.016959,
-    "std": 0.051088,
-    "sem": 0.001351,
+    "count": 1429,
+    "mean": 0.035154,
+    "median": 0.01696,
+    "std": 0.050615,
+    "sem": 0.001339,
     "min": 0.000713,
     "max": 0.397255,
-    "recall-0.025": 0.67643,
+    "recall-0.025": 0.675732,
     "recall-0.05": 0.824268,
-    "recall-0.1": 0.90795,
-    "recall-0.15": 0.954672,
-    "recall-0.25": 0.987448,
-    "recall-0.5": 0.997211,
+    "recall-0.1": 0.909344,
+    "recall-0.15": 0.95537,
+    "recall-0.25": 0.98675,
+    "recall-0.5": 0.996513,
     "num_labels": 1434
   },
   "wrist_right": {
     "count": 1456,
-    "mean": 0.024355,
+    "mean": 0.024195,
     "median": 0.016346,
-    "std": 0.024627,
-    "sem": 0.000646,
+    "std": 0.023585,
+    "sem": 0.000618,
     "min": 0.001905,
-    "max": 0.304244,
-    "recall-0.025": 0.70261,
+    "max": 0.202973,
+    "recall-0.025": 0.701923,
     "recall-0.05": 0.89217,
-    "recall-0.1": 0.979396,
-    "recall-0.15": 0.993819,
-    "recall-0.25": 0.999313,
+    "recall-0.1": 0.980082,
+    "recall-0.15": 0.994505,
+    "recall-0.25": 1.0,
     "recall-0.5": 1.0,
     "num_labels": 1456
   },
   "hip_left": {
     "count": 1463,
-    "mean": 0.059443,
+    "mean": 0.059397,
     "median": 0.053492,
-    "std": 0.030125,
-    "sem": 0.000788,
+    "std": 0.02998,
+    "sem": 0.000784,
     "min": 0.005563,
     "max": 0.17979,
-    "recall-0.025": 0.068259,
-    "recall-0.05": 0.424573,
+    "recall-0.025": 0.069625,
+    "recall-0.05": 0.423208,
     "recall-0.1": 0.893515,
-    "recall-0.15": 0.968601,
+    "recall-0.15": 0.969283,
     "recall-0.25": 0.998635,
     "recall-0.5": 0.998635,
     "num_labels": 1465
   },
   "hip_right": {
     "count": 1464,
-    "mean": 0.058641,
-    "median": 0.054688,
-    "std": 0.029005,
-    "sem": 0.000758,
+    "mean": 0.058556,
+    "median": 0.054479,
+    "std": 0.028879,
+    "sem": 0.000755,
     "min": 0.003776,
-    "max": 0.304524,
-    "recall-0.025": 0.055252,
-    "recall-0.05": 0.417462,
-    "recall-0.1": 0.91337,
-    "recall-0.15": 0.985675,
+    "max": 0.304525,
+    "recall-0.025": 0.05457,
+    "recall-0.05": 0.420191,
+    "recall-0.1": 0.914734,
+    "recall-0.15": 0.986357,
     "recall-0.25": 0.997954,
     "recall-0.5": 0.998636,
     "num_labels": 1466
   },
   "knee_left": {
     "count": 1463,
-    "mean": 0.0519,
-    "median": 0.042755,
-    "std": 0.042761,
-    "sem": 0.001118,
+    "mean": 0.051823,
+    "median": 0.042757,
+    "std": 0.042167,
+    "sem": 0.001103,
     "min": 0.001938,
-    "max": 0.353024,
-    "recall-0.025": 0.186348,
-    "recall-0.05": 0.635495,
+    "max": 0.3408,
+    "recall-0.025": 0.187713,
+    "recall-0.05": 0.63413,
     "recall-0.1": 0.927645,
-    "recall-0.15": 0.967235,
-    "recall-0.25": 0.9843,
+    "recall-0.15": 0.967918,
+    "recall-0.25": 0.984983,
     "recall-0.5": 0.998635,
     "num_labels": 1465
   },
   "knee_right": {
     "count": 1457,
-    "mean": 0.048275,
+    "mean": 0.048418,
     "median": 0.042583,
-    "std": 0.029088,
-    "sem": 0.000762,
-    "min": 0.003542,
-    "max": 0.324496,
-    "recall-0.025": 0.214531,
+    "std": 0.029681,
+    "sem": 0.000778,
+    "min": 0.003543,
+    "max": 0.324497,
+    "recall-0.025": 0.213845,
     "recall-0.05": 0.588759,
-    "recall-0.1": 0.938999,
-    "recall-0.15": 0.993146,
-    "recall-0.25": 0.997258,
+    "recall-0.1": 0.938314,
+    "recall-0.15": 0.992461,
+    "recall-0.25": 0.996573,
     "recall-0.5": 0.998629,
     "num_labels": 1459
   },
   "ankle_left": {
     "count": 1456,
-    "mean": 0.083562,
-    "median": 0.037346,
-    "std": 0.106393,
-    "sem": 0.002789,
+    "mean": 0.08324,
+    "median": 0.037284,
+    "std": 0.106306,
+    "sem": 0.002787,
     "min": 0.002453,
-    "max": 0.495773,
-    "recall-0.025": 0.361586,
-    "recall-0.05": 0.58715,
-    "recall-0.1": 0.729323,
-    "recall-0.15": 0.820232,
-    "recall-0.25": 0.913192,
+    "max": 0.495774,
+    "recall-0.025": 0.362953,
+    "recall-0.05": 0.588517,
+    "recall-0.1": 0.732057,
+    "recall-0.15": 0.821599,
+    "recall-0.25": 0.913876,
     "recall-0.5": 0.995215,
     "num_labels": 1463
   },
   "ankle_right": {
-    "count": 1448,
-    "mean": 0.077433,
-    "median": 0.033338,
-    "std": 0.10331,
-    "sem": 0.002716,
+    "count": 1449,
+    "mean": 0.077884,
+    "median": 0.033398,
+    "std": 0.103863,
+    "sem": 0.002729,
     "min": 0.001061,
     "max": 0.4981,
     "recall-0.025": 0.369863,
-    "recall-0.05": 0.686301,
-    "recall-0.1": 0.773288,
-    "recall-0.15": 0.815068,
+    "recall-0.05": 0.684932,
+    "recall-0.1": 0.772603,
+    "recall-0.15": 0.813699,
     "recall-0.25": 0.896575,
-    "recall-0.5": 0.991781,
+    "recall-0.5": 0.992466,
     "num_labels": 1460
   },
   "joint_recalls": {
     "num_labels": 18990,
-    "recall-0.025": 0.48768,
+    "recall-0.025": 0.48752,
     "recall-0.05": 0.75034,
-    "recall-0.1": 0.9247,
-    "recall-0.15": 0.95935,
-    "recall-0.25": 0.98146,
-    "recall-0.5": 0.99774
+    "recall-0.1": 0.92507,
+    "recall-0.15": 0.95956,
+    "recall-0.25": 0.98167,
+    "recall-0.5": 0.99768
   }
 }
 {
   "total_parts": 20444,
-  "correct_parts": 19975,
-  "pcp": 0.977059
+  "correct_parts": 19980,
+  "pcp": 0.977304
 }
 ```
 
diff --git a/scripts/test_skelda_dataset.py b/scripts/test_skelda_dataset.py
index 35cd3d4..0cf8a10 100644
--- a/scripts/test_skelda_dataset.py
+++ b/scripts/test_skelda_dataset.py
@@ -384,7 +384,7 @@ def main():
             poses3D = np.zeros([1, len(joint_names_3d), 4]).tolist()
         else:
             spt_cameras = spt.convert_cameras(label["cameras"])
-            roomparams = [label["room_center"], label["room_size"]]
+            roomparams = [label["room_size"], label["room_center"]]
             poses3D = triangulator.triangulate_poses(
                 poses_2d, spt_cameras, roomparams, joint_names_2d
             )
diff --git a/spt/interface.hpp b/spt/interface.hpp
index 0fb45ab..8079dad 100644
--- a/spt/interface.hpp
+++ b/spt/interface.hpp
@@ -30,7 +30,7 @@ public:
      *
      * @param poses_2d List of shape [views, persons, joints, 3], containing the 2D poses.
      * @param cameras List of cameras.
-     * @param roomparams Room parameters (room center, room size).
+     * @param roomparams Room parameters (room size, room center).
      * @param joint_names List of 2D joint names.
      *
      * @return List of shape [persons, joints, 4], containing the 3D poses.
diff --git a/spt/triangulator.cpp b/spt/triangulator.cpp
index c506ba2..903df1b 100644
--- a/spt/triangulator.cpp
+++ b/spt/triangulator.cpp
@@ -925,8 +925,8 @@ std::pair<cv::Mat, float> TriangulatorInternal::triangulate_and_score(
     {
         mean[j] *= inv_num_vis;
     }
-    const std::array<float, 3> &center = roomparams[0];
-    const std::array<float, 3> &size = roomparams[1];
+    const std::array<float, 3> &size = roomparams[0];
+    const std::array<float, 3> &center = roomparams[1];
     for (int j = 0; j < 3; ++j)
     {
         if (mean[j] > center[j] + size[j] / 2.0 ||
@@ -1061,9 +1061,7 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
 {
     float max_center_dist_sq = 0.6 * 0.6;
     float max_joint_avg_dist = 0.3;
-
     size_t num_pairs = all_pairs.size();
-    size_t num_joints = all_scored_poses[0].first.rows;
 
     // Calculate pose centers
     std::vector<cv::Point3d> centers;
@@ -1071,6 +1069,8 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
     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);
         size_t num_valid = 0;
         for (size_t j = 0; j < num_joints; ++j)
@@ -1102,6 +1102,8 @@ std::vector<std::tuple<cv::Point3d, cv::Mat, std::vector<int>>> TriangulatorInte
     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];
         float best_dist = std::numeric_limits<float>::infinity();
         int best_group = -1;
@@ -1512,7 +1514,7 @@ void TriangulatorInternal::filter_poses(
         {
             // Check if the mean position is outside the room boundaries
             if (mean[j] > room_half_size[j] + room_center[j] ||
-                mean[j] < -room_half_size[j] - room_center[j])
+                mean[j] < -room_half_size[j] + room_center[j])
             {
                 outside = true;
                 break;
@@ -1522,7 +1524,7 @@ void TriangulatorInternal::filter_poses(
         {
             // Check if any limb is too far outside the room
             if (maxs[j] > room_half_size[j] + room_center[j] + wdist ||
-                mins[j] < -room_half_size[j] - room_center[j] - wdist)
+                mins[j] < -room_half_size[j] + room_center[j] - wdist)
             {
                 outside = true;
                 break;
@@ -1540,8 +1542,8 @@ void TriangulatorInternal::filter_poses(
             size_t found_limbs = 0;
             for (size_t j = 0; j < core_limbs_idx.size(); ++j)
             {
-                size_t start_idx = core_limbs_idx[j][0];
-                size_t end_idx = core_limbs_idx[j][1];
+                size_t start_idx = core_joint_idx[core_limbs_idx[j][0]];
+                size_t end_idx = core_joint_idx[core_limbs_idx[j][1]];
 
                 float *joint_start_ptr = pose.ptr<float>(start_idx);
                 float *joint_end_ptr = pose.ptr<float>(end_idx);
@@ -1565,8 +1567,8 @@ void TriangulatorInternal::filter_poses(
         int total_limbs = 0;
         for (size_t j = 0; j < core_limbs_idx.size(); ++j)
         {
-            size_t start_idx = core_limbs_idx[j][0];
-            size_t end_idx = core_limbs_idx[j][1];
+            size_t start_idx = core_joint_idx[core_limbs_idx[j][0]];
+            size_t end_idx = core_joint_idx[core_limbs_idx[j][1]];
 
             float *joint_start_ptr = pose.ptr<float>(start_idx);
             float *joint_end_ptr = pose.ptr<float>(end_idx);
@@ -1727,7 +1729,7 @@ void TriangulatorInternal::add_missing_joints(
                 if (adjacents.find(adname) != adjacents.end())
                 {
                     // Joint is missing; attempt to estimate its position based on adjacent joints
-                    std::array<float, 3> adjacent_position;
+                    std::array<float, 3> adjacent_position = {0.0f, 0.0f, 0.0f};
                     size_t adjacent_count = 0;
 
                     const std::vector<std::string> &adjacent_joint_names = adjacents[adname];
diff --git a/tests/test_interface.py b/tests/test_interface.py
index 1915977..acf80ff 100644
--- a/tests/test_interface.py
+++ b/tests/test_interface.py
@@ -26,7 +26,7 @@ def main():
     print("")
 
     # Load input data
-    roomparams = [[0, 0, 1.0], [4.8, 6.0, 2.0]]
+    roomparams = [[4.8, 6.0, 2.0], [0, 0, 1.0]]
     joint_names = [
         "nose",
         "eye_left",
@@ -70,7 +70,7 @@ def main():
     print("")
 
     # Load input data
-    roomparams = [[0, -0.5, 1.2], [5.6, 6.4, 2.4]]
+    roomparams = [[5.6, 6.4, 2.4], [0, -0.5, 1.2]]
     cpath = "/SimplePoseTriangulation/data/p1/sample.json"
     ppath = "/SimplePoseTriangulation/tests/poses_p1.json"
     with open(cpath, "r") as file: