diff --git a/pytorch3d/implicitron/dataset/orm_types.py b/pytorch3d/implicitron/dataset/orm_types.py
index 5736ab4ba..2e916021a 100644
--- a/pytorch3d/implicitron/dataset/orm_types.py
+++ b/pytorch3d/implicitron/dataset/orm_types.py
@@ -33,7 +33,35 @@
 
 
 # these produce policies to serialize structured types to blobs
-def ArrayTypeFactory(shape):
+def ArrayTypeFactory(shape=None):
+    if shape is None:
+
+        class VariableShapeNumpyArrayType(TypeDecorator):
+            impl = LargeBinary
+
+            def process_bind_param(self, value, dialect):
+                if value is None:
+                    return None
+
+                ndim_bytes = np.int32(value.ndim).tobytes()
+                shape_bytes = np.array(value.shape, dtype=np.int64).tobytes()
+                value_bytes = value.astype(np.float32).tobytes()
+                return ndim_bytes + shape_bytes + value_bytes
+
+            def process_result_value(self, value, dialect):
+                if value is None:
+                    return None
+
+                ndim = np.frombuffer(value[:4], dtype=np.int32)[0]
+                value_start = 4 + 8 * ndim
+                shape = np.frombuffer(value[4:value_start], dtype=np.int64)
+                assert shape.shape == (ndim,)
+                return np.frombuffer(value[value_start:], dtype=np.float32).reshape(
+                    shape
+                )
+
+        return VariableShapeNumpyArrayType
+
     class NumpyArrayType(TypeDecorator):
         impl = LargeBinary
 
@@ -158,4 +186,4 @@ class SqlSequenceAnnotation(Base):
         mapped_column("_point_cloud_n_points", nullable=True),
     )
     # the bigger the better
-    viewpoint_quality_score: Mapped[Optional[float]] = mapped_column(default=None)
+    viewpoint_quality_score: Mapped[Optional[float]] = mapped_column()
diff --git a/pytorch3d/implicitron/dataset/sql_dataset.py b/pytorch3d/implicitron/dataset/sql_dataset.py
index 4c9d3bb5e..2c74e56c5 100644
--- a/pytorch3d/implicitron/dataset/sql_dataset.py
+++ b/pytorch3d/implicitron/dataset/sql_dataset.py
@@ -142,8 +142,10 @@ def __post_init__(self) -> None:
         run_auto_creation(self)
         self.frame_data_builder.path_manager = self.path_manager
 
-        # pyre-ignore
-        self._sql_engine = sa.create_engine(f"sqlite:///{self.sqlite_metadata_file}")
+        # pyre-ignore  # NOTE: sqlite-specific args (read-only mode).
+        self._sql_engine = sa.create_engine(
+            f"sqlite:///file:{self.sqlite_metadata_file}?mode=ro&uri=true"
+        )
 
         sequences = self._get_filtered_sequences_if_any()
 
diff --git a/tests/implicitron/test_orm_types.py b/tests/implicitron/test_orm_types.py
index 7570b002b..e6f94c010 100644
--- a/tests/implicitron/test_orm_types.py
+++ b/tests/implicitron/test_orm_types.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 
-from pytorch3d.implicitron.dataset.orm_types import TupleTypeFactory
+from pytorch3d.implicitron.dataset.orm_types import ArrayTypeFactory, TupleTypeFactory
 
 
 class TestOrmTypes(unittest.TestCase):
@@ -35,3 +35,28 @@ def test_tuple_serialization_2d(self):
         self.assertEqual(type(input_hat[0][0]), type(input_tuple[0][0]))
         # we use float32 to serialise
         np.testing.assert_almost_equal(input_hat, input_tuple, decimal=6)
+
+    def test_array_serialization_none(self):
+        ttype = ArrayTypeFactory((3, 3))()
+        output = ttype.process_bind_param(None, None)
+        self.assertIsNone(output)
+        output = ttype.process_result_value(output, None)
+        self.assertIsNone(output)
+
+    def test_array_serialization(self):
+        for input_list in [[1, 2, 3], [[4.5, 6.7], [8.9, 10.0]]]:
+            input_array = np.array(input_list)
+
+            # first, dynamic-size array
+            ttype = ArrayTypeFactory()()
+            output = ttype.process_bind_param(input_array, None)
+            input_hat = ttype.process_result_value(output, None)
+            self.assertEqual(input_hat.dtype, np.float32)
+            np.testing.assert_almost_equal(input_hat, input_array, decimal=6)
+
+            # second, fixed-size array
+            ttype = ArrayTypeFactory(tuple(input_array.shape))()
+            output = ttype.process_bind_param(input_array, None)
+            input_hat = ttype.process_result_value(output, None)
+            self.assertEqual(input_hat.dtype, np.float32)
+            np.testing.assert_almost_equal(input_hat, input_array, decimal=6)