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.

src/snapform/properties.py

@@ -76,7 +76,7 @@ class SnapFormSettings(bpy.types.PropertyGroup):
         name="Model",
         description="MoGe-2 model used for depth estimation",
         items=moge2_model_enum_items,
-        default=1,
+        default=0,
     )
     moge_export_format: bpy.props.EnumProperty(
         name="Output Format",
@@ -110,7 +110,7 @@ class SnapFormSettings(bpy.types.PropertyGroup):
     )
     use_ray_pose: bpy.props.BoolProperty(
         name="Use Ray Pose",
-        description="Use the slower and more accurate ray-based pose estimation",
+        description="Legacy task compatibility; ONNX uses the camera decoder",
         default=False,
     )
     ref_view_strategy: bpy.props.EnumProperty(