diff --git a/python-sdk/nuscenes/eval/panoptic/README.md b/python-sdk/nuscenes/eval/panoptic/README.md
index ae8ee1f7..1c57c7e8 100644
--- a/python-sdk/nuscenes/eval/panoptic/README.md
+++ b/python-sdk/nuscenes/eval/panoptic/README.md
@@ -207,7 +207,7 @@ different segments.
 
 ### Multi-Object Panoptic Tracking
 #### Panoptic Tracking Quality (PTQ)
-We use PTQ [Juana et al.](https://arxiv.org/pdf/2004.08189.pdf) that extends PQ with the IoU of matched segments with
+We use PTQ [Hurtado et al.](https://arxiv.org/pdf/2004.08189.pdf) that extends PQ with the IoU of matched segments with
 track ID discrepancy, penalizing the incorrect track predictions. PTQ is defined as
 (∑{**1**(p,g) IoU(p,g)} - |IDS|) / (|TP|+ 0.5|FP|+ 0.5|FN|), where IDS stands for ID switches, and it is computed as
 the number of true positives (TP) that differ between tracking prediction and ground truth.
diff --git a/python-sdk/nuscenes/panoptic/generate_panoptic_labels.py b/python-sdk/nuscenes/panoptic/generate_panoptic_labels.py
index d8aad118..ba5ee5b6 100644
--- a/python-sdk/nuscenes/panoptic/generate_panoptic_labels.py
+++ b/python-sdk/nuscenes/panoptic/generate_panoptic_labels.py
@@ -15,6 +15,7 @@
 from tqdm import tqdm
 
 from nuscenes.nuscenes import NuScenes
+from nuscenes.panoptic.panoptic_utils import STUFF_START_CLASS_ID
 from nuscenes.utils.data_classes import LidarSegPointCloud
 from nuscenes.utils.geometry_utils import points_in_box
 
@@ -70,7 +71,15 @@ def generate_panoptic_labels(nusc: NuScenes, out_dir: str, verbose: bool = False
                     panop_labels[indices[index]] += instance_id
                     overlap_box_count[indices[index]] += 1
 
-        panop_labels[overlap_box_count > 1] = 0
+        panop_labels[overlap_box_count > 1] = 0  # Set pixels overlapped by > 1 boxes to 0.
+
+        # Thing pixels that are not inside any box have instance id == 0, reset them to 0.
+        # For these pixels that have thing semantic classes, but do not fall inside any annotation box, set their
+        # panoptic label to 0.
+        semantic_labels = panop_labels // 1000
+        thing_mask = np.logical_and(semantic_labels > 0, semantic_labels < STUFF_START_CLASS_ID)
+        pixels_wo_box = np.logical_and(thing_mask, panop_labels % 1000 == 0)
+        panop_labels[pixels_wo_box] = 0
 
         panoptic_file = nusc.get('lidarseg', lidar_token)['filename'].split('/')[-1]
         panoptic_file = panoptic_file.replace('lidarseg', 'panoptic')