- 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.
- Create a build workflow in `.github/workflows/build.yml` to automate the build process.
- Configure Python environment and install dependencies.
- Add steps for running tests and packaging the extension.
chore: Initialize project metadata and dependencies for Depth Anything 3
- Add `pyproject.toml` for project configuration and dependencies.
- Include optional dependencies for app and gs functionalities.
- Define project scripts and metadata.
chore: Add requirements file for Depth Anything 3 dependencies
- Create `requirements.txt` listing all necessary packages for the project.
feat: Implement export utility functions for Depth Anything 3
- Create `src/depth_anything_3/utils/export/__init__.py` to handle various export formats.
- Implement functions for exporting predictions in different formats.
feat: Add ray utility functions for homography estimation
- Create `src/depth_anything_3/utils/ray_utils.py` with functions for computing optimal rotation and homography using RANSAC.
feat: Implement vendor preparation script
- Add `prepare_vendor.py` to manage third-party dependencies and artifacts.
- Include functions for downloading and verifying artifacts based on a lock file.
test: Add unit tests for vendor preparation script
- Create `tests/test_prepare_vendor.py` to test artifact downloading and verification.
- Implement tests for handling hash mismatches and unsupported platforms.
chore: Add vendor lock file for dependency management
- Create `vendor.lock.toml` to define artifacts and platforms for the project.
