OpenCV4.0已經發布一段時間，更新在官方提示中，重要的是添加了ONNX接口和KinectFusion算法；

Release highlights:

• OpenCV is now C++11 library and requires C++11-compliant compiler. Minimum required CMake version has been raised to 3.5.1.
• A lot of C API from OpenCV 1.x has been removed.
• Persistence (storing and loading structured data to/from XML, YAML or JSON) in the core module has been completely reimplemented in C++ and lost the C API as well.
• New module G-API has been added, it acts as an engine for very efficient graph-based image procesing pipelines.
• dnn module was updated with Deep Learning Deployment Toolkit from the OpenVINO? toolkit R4. See the guide how to build and use OpenCV with DLDT support.
• dnn module now includes experimental Vulkan backend and supports networks in ONNX format.
• The popular Kinect Fusion algorithm has been implemented and optimized for CPU and GPU (OpenCL)
• QR code detector and decoder have been added to the objdetect module
• Very efficient and yet high-quality DIS dense optical flow algorithm has been moved from opencv_contrib to the video module.
• More details can be found in previous announces: 4.0-alpha, 4.0-beta, 4.0-rc and in the changelog

Branch 3.4 will be switched to maintanence mode: only bugfixes and light features will be accepted. BTW, release 3.4.4 is ready too!

重要更新：

??????? OpenCV 4.0 現在是一個 C++11 庫，要求 C++11 兼容的編譯器。所需的 CMake 至少是 3.5.1 版本。

??????? 移除 OpenCV 1.x 中的大量 C API。core 模塊中的 Persistence（用于存儲和加載 XML、YAML 或 JSON 格式的結構化數據）可以完全使用 C++ 來重新實現，因此這里的 C API 也被移除。

??????? 添加了新模塊 G-API，它可作為基于圖的高效圖像處理流程。

??????? dnn 模塊包括實驗用 Vulkan 后端，且支持 ONNX 格式的網絡。

??????? 實現了流行的 Kinect Fusion 算法，且為 CPU 和 GPU (OpenCL) 進行優化。

??????? objdetect 模塊中添加了二維碼檢測器和解碼器。將高效、高質量的 DIS dense optical flow 算法從 opencv_contrib 移到 video 模塊。

??????? 此外，OpenCV 4.0 支持 Mask-RCNN 模型，性能也有所提升，圖像處理操作可實現 15%-30% 的速度提升。

總結

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