CV-CUDA Release v0.5.0

CV-CUDA 0.5.0 Release Notes

CV-CUDA 0.5.0 is a major release of the library providing multiple new operators, features, and fixes to multiple customer-reported issues.

Release Highlights

CV-CUDA v0.5.0 includes the following key changes:

• New Operators:

  • FindHomography: Calculates a perspective transform from four pairs of the corresponding points
  • Label: Labels connected regions in an image using 4-way connectivity for foreground and 8-way for background pixels
  • PairwiseMatcher: Matches features computed separately (e.g. via the SIFT operator) in two images using the brute force method
  • Stack: Concatenates two input tensors into a single output tensor

• New Features:

  • Added TensorBatch in C++ and Python, a container type that can hold a list of non-uniformly shaped tensors
  • Added Workspace in C++ and Python, an abstraction of memory and asynchronous resources for CV-CUDA operators
  • Added better color format support in nvcv_types
  • New sample application for the Label operator
  • JetPack 5.1.2 support for L4T (Jetson Orin, L4T 35.4.1, CUDA 11.4)
  • Enhanced documentation

• Bug Fixes:

  • Resolved memory leak in NvBlurBoxes
  • Fixed segmentation fault issue in Python with certain imports
  • Corrected typestr format issue in __cuda_array_interface__
  • Addressed occasional hanging in OpBoxBlur on RGBA images

Compatibility

• GPU Compute Capability: 7+.x
• Ubuntu x86_64: 20.04, 22.04
• CUDA Toolkit: 11.7+ (11.2+ for library build and run)
• L4T: 35.4.1, JetPack 5.1.2 aarch64
• GCC: 11.0+ (9.x and 10.x for APIs with pre-built binary)
• Python: 3.8, 3.10

Known Issues/Limitations

• For GCC versions lower than 11.0, C++17 support needs to be enabled when compiling CV-CUDA.

License

CV-CUDA is licensed under the Apache 2.0 license.

Resources

1. CV-CUDA GitHub
2. CV-CUDA Increasing Throughput and Reducing Costs for AI-Based Computer Vision with CV-CUDA
3. NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI
4. CV-CUDA helps Tencent Cloud audio and video PaaS platform achieve full-process GPU acceleration for video enhancement AI

Acknowledgements

CV-CUDA is developed jointly by NVIDIA and the ByteDance Machine Learning team.