Top 5 - Game Engines Of The Future

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glTF loader for Android Vulkan

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This post is going to introduce how we integrate with an existing glTF loader library to make it be able to show glTF models in our Vulkan rendering framework,  vulkan-android . tinygltf In the beginning, we don't want to make our new own wheel, so choosing  tinygltf as our glTF loader. tinygltf is a C++11 based library that would help us support all possible cross-platform project easily. tinygltf setup in Android Studio In  vulkan-android  project, we put third party libraries into third_party folder. Therefore, we need to include tinygltf from third_party folder in app/CMakeLists.txt as below. set(THIRD_PARTY_DIR ../../third_party) include_directories(${THIRD_PARTY_DIR}/tinygltf) Model loading from tinygltf We are going to load a gltf ASCII or binary format model from the storage. tinygltf provides two APIs , they are LoadBinaryFromFile() and LoadASCIIFromFile() respectively based on the extension name is *.glb or *.gltf. TinyGLTF loader will return a tiny...
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Drawing textured cube with Vulkan on Android

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Vulkan is a modern hardware-accelerated Graphics API. Its goal is providing an high efficient way in low-level graphics and compute on modern GPUs for PC, mobile, and embedded devices. I am personally working a self training project, vulkan-android , to teach myself how to use this new APIs. The difference between OpenGL and Vulkan OpenGL: Higher level API in comparison with Vulkan, and the next generation of OpenGL 4 will be Vulkan.   Cross-platform and Cross-language (mostly still based on C/C++, but people implemented diverse versions and expose similar API binding based on OpenGL C++, WebGL is a good example).  Mainly used in 3D graphics and 2D image processing to interact with GPU in order to achieve hardware acceleration. Don't have a command buffer can be manipulated at the application side. That means we will be easily see draw calls being the performance bottleneck in a big and complex 3D scene. Vulkan: Cross-platform and low-overhead. Erase the boundary bet...
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How to train custom objects in YOLOv2

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This article is based on [1]. We wanna a way to train the object tags that we are interested. Darknet has a Windows version that is ported by AlexeyAB [2]. First of all, we need to build darknet.exe from AlexeyAB to help us train and test data. Go to build/darknet, using VS 2015 to open darknet.sln, and config it to x64 solution platform. Rebuild solution! It should be success to generate darknet.exe. Then, we need to label objects from images that are used for training data. I use BBox label tool to help me label objects' coordinates in images for training data. (python ./main.py) This tool's image root folder is at ./Images, we can create a sub-folder ( 002 ) and insert 002 to let this tool load all *.jpg files from there. We will mark labels in this tool to help us generate objects' region to mark where objects are. The outputs are the image-space coordinate in images and stored at ./Labels/002 . However, the format of this coordinate is different from YOLOv2, YOLOv2...
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