diff --git a/pcdet/datasets/once/once_eval/eval_utils.py b/pcdet/datasets/once/once_eval/eval_utils.py
new file mode 100644
index 0000000..4526392
--- /dev/null
+++ b/pcdet/datasets/once/once_eval/eval_utils.py
@@ -0,0 +1,53 @@
+import numpy as np
+
+
+def compute_split_parts(num_samples, num_parts):
+    part_samples = num_samples // num_parts
+    remain_samples = num_samples % num_parts
+    if part_samples == 0:
+        return [num_samples]
+    if remain_samples == 0:
+        return [part_samples] * num_parts
+    else:
+        return [part_samples] * num_parts + [remain_samples]
+
+
+def overall_filter(boxes):
+    ignore = np.zeros(boxes.shape[0], dtype=bool)  # all false
+    return ignore
+
+
+def distance_filter(boxes, level):
+    ignore = np.ones(boxes.shape[0], dtype=bool)  # all true
+    dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
+
+    if level == 0:  # 0-30m
+        flag = dist < 30
+    elif level == 1:  # 30-50m
+        flag = (dist >= 30) & (dist < 50)
+    elif level == 2:  # 50m-inf
+        flag = dist >= 50
+    else:
+        assert False, 'level < 3 for distance metric, found level %s' % (str(level))
+
+    ignore[flag] = False
+    return ignore
+
+
+def overall_distance_filter(boxes, level):
+    ignore = np.ones(boxes.shape[0], dtype=bool)  # all true
+    dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
+
+    if level == 0:
+        flag = np.ones(boxes.shape[0], dtype=bool)
+    elif level == 1:  # 0-30m
+        flag = dist < 30
+    elif level == 2:  # 30-50m
+        flag = (dist >= 30) & (dist < 50)
+    elif level == 3:  # 50m-inf
+        flag = dist >= 50
+    else:
+        assert False, 'level < 4 for overall & distance metric, found level %s' % (str(level))
+
+    ignore[flag] = False
+    return ignore
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