feat: Implement ONNX model inference for BEN2, Depth Anything 3, and MoGe

- Added `ben2_onnx.py` for BEN2 ONNX inference with preprocessing.
- Introduced `da3_onnx.py` for Depth Anything 3 inference, including camera projection calculations.
- Created `moge_onnx.py` for MoGe-2 ONNX inference and post-processing.
- Implemented shared ONNX Runtime session helpers in `onnx_runtime.py`.
- Added model synchronization script `sync_models.py` for downloading and verifying ONNX models.
- Developed unit tests for BEN2, Depth Anything 3, and MoGe ONNX models to ensure functionality.
- Updated `vendor.lock.toml` to remove unused artifacts.

tests/test_da3_onnx.py

@@ -0,0 +1,61 @@
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from types import SimpleNamespace
+
+import numpy as np
+from PIL import Image
+
+
+SOURCE_DIR = Path(__file__).resolve().parents[1] / "src" / "snapform"
+sys.path.insert(0, str(SOURCE_DIR))
+
+import da3_onnx
+
+
+class FakeSession:
+    def get_inputs(self):
+        return [SimpleNamespace(name="images"), SimpleNamespace(name="strategy")]
+
+    def run(self, output_names, feeds):
+        self.feeds = feeds
+        _, views, _, height, width = feeds["images"].shape
+        depth = np.ones((1, views, height, width), dtype=np.float32)
+        confidence = np.full_like(depth, 0.75)
+        pose = np.zeros((1, views, 9), dtype=np.float32)
+        pose[..., 6] = 1.0
+        pose[..., 7:] = np.pi / 2.0
+        return depth, confidence, pose
+
+
+class Da3OnnxTest(unittest.TestCase):
+    def test_target_size_uses_patch_multiple(self):
+        self.assertEqual(
+            da3_onnx.target_size((800, 600), 504, "upper_bound_resize"),
+            (504, 378),
+        )
+
+    def test_infer_returns_camera_and_image_arrays(self):
+        with tempfile.TemporaryDirectory() as directory:
+            image_path = Path(directory) / "input.png"
+            Image.new("RGB", (80, 60), (10, 20, 30)).save(image_path)
+            session = FakeSession()
+
+            result = da3_onnx.infer(
+                session,
+                [image_path],
+                process_res=56,
+                ref_view_strategy="middle",
+            )
+
+        self.assertEqual(result.depth.shape, (1, 42, 56))
+        self.assertEqual(result.conf.shape, result.depth.shape)
+        self.assertEqual(result.processed_images.shape, (1, 42, 56, 3))
+        self.assertEqual(result.extrinsics.shape, (1, 3, 4))
+        self.assertEqual(result.intrinsics.shape, (1, 3, 3))
+        self.assertEqual(int(session.feeds["strategy"][0]), 1)
+        np.testing.assert_allclose(result.extrinsics[0, :3, :3], np.eye(3))
+
+if __name__ == "__main__":
+    unittest.main()