diff --git a/gtsam/slam/dataset.cpp b/gtsam/slam/dataset.cpp
index 0f4f24bd0..0bf5f4f5b 100644
--- a/gtsam/slam/dataset.cpp
+++ b/gtsam/slam/dataset.cpp
@@ -102,26 +102,28 @@ static SharedNoiseModel readNoiseModel(ifstream& is, bool smart,
   double v1, v2, v3, v4, v5, v6;
   is >> v1 >> v2 >> v3 >> v4 >> v5 >> v6;
 
+   if (noiseFormat == NoiseFormatAUTO)
+   {
+     // Try to guess covariance matrix layout
+     if(v1 != 0.0 && v2 == 0.0 && v3 != 0.0 && v4 != 0.0 && v5 == 0.0 && v6 == 0.0)
+     {
+       // NoiseFormatGRAPH
+       noiseFormat = NoiseFormatGRAPH;
+     }
+     else if(v1 != 0.0 && v2 == 0.0 && v3 == 0.0 && v4 != 0.0 && v5 == 0.0 && v6 != 0.0)
+     {
+       // NoiseFormatCOV
+       noiseFormat = NoiseFormatCOV;
+     }
+     else
+     {
+       throw std::invalid_argument("load2D: unrecognized covariance matrix format in dataset file. Please specify the noise format.");
+     }
+  }
+
   // Read matrix and check that diagonal entries are non-zero
   Matrix M(3, 3);
   switch (noiseFormat) {
-  case NoiseFormatAUTO:
-    // Try to guess covariance matrix layout
-    if(v1 != 0.0 && v2 == 0.0 && v3 != 0.0 && v4 != 0.0 && v5 == 0.0 && v6 == 0.0)
-    {
-      // NoiseFormatGRAPH
-      M << v1, v2, v5, v2, v3, v6, v5, v6, v4;
-    }
-    else if(v1 != 0.0 && v2 == 0.0 && v3 == 0.0 && v4 != 0.0 && v5 == 0.0 && v6 != 0.0)
-    {
-      // NoiseFormatCOV
-      M << v1, v2, v3, v2, v4, v5, v3, v5, v6;
-    }
-    else
-    {
-      throw std::invalid_argument("load2D: unrecognized covariance matrix format in dataset file. Please specify the noise format.");
-    }
-    break;
   case NoiseFormatG2O:
   case NoiseFormatCOV:
     // i.e., [ v1 v2 v3; v2' v4 v5; v3' v5' v6 ]
@@ -153,7 +155,6 @@ static SharedNoiseModel readNoiseModel(ifstream& is, bool smart,
     // In both cases, what is stored in file is the information matrix
     model = noiseModel::Gaussian::Information(M, smart);
     break;
-  case NoiseFormatAUTO:
   case NoiseFormatGRAPH:
   case NoiseFormatCOV:
     // These cases expect covariance matrix