![]() ![]() The number of vertices your Landscape is using. ![]() Going over this cap could result in performance issues with the Landscape. This value is capped at 32 x 32 since each component has a CPU cost associated with it. Sets the size of your landscape along with Section Size. These issues could be even worse on mobile devices because of the limited amount of draw calls you can have due to hardware limitations. This could be especially prevalent when using very large areas of Landscape. However, you might run into issues with the Landscape rendering too many vertices at once. ![]() With a larger section size, you get the added benefit of reduced CPU calculation time. One component could have 2 x 2 sections, which means that it is possible that one component could be rendering four different LODs at once. Each section is the basic unit for the Landscape LOD. If you want to have a large Landscape, you will need to use a larger section size, since using a smaller section size and then scaling the Landscape will increase the cost on the CPU. Larger sections have fewer components and are less costly on the CPU. Landscapes with smaller sections can have more optimized LODs but at a higher CPU cost. Landscapes use Section Size for LOD and Culling. Sets the scale of the Landscape in the world. Sets the rotation of the Landscape in the world. Sets the location in the world where the Landscape is created. Imports a Landscape heightmap made in an external program.Įnables the use of Non-Destructive Landscape Layers and Splines.ĭisplays any layers that are a part of your Landscape Material. In the broad sense, the actual encoding of the image is just moving some bits around in the end, so the hardware guys don’t get real excited about that, they’re just happy to get the bits out in the first place.Creates a new Landscape Actor in your Level. Typically imaging is easy when there is a bunch of light about, a lot of tradeoffs get made as things get darker to minimize the noise in the image or deal with moire effects on small, high resolution imagers. That’s where you can imagine subsampling and luminance ‘cheats’ for better overall image quality or low light performance. When you have a stream of 4K video running 60fps, the requirements are obviously a lot different than So there are all sorts of tradeoffs the engineers make (especially on inexpensive cameras) to get the best image/performance possible at a given price point. You’ll see that in DSLR types of cameras all the time where devices with fewer actual pixels have significantly better picture quality because larger sensor elements can gather more light.Īnother thing to take into account is how fast the images have to be acquired. ![]() But the size of the actual light sensing elements decreased. As the marketing race to more megapixels started to heat up, the more pixel elements there were per die. I’m sure you’re familiar with the idea of a small imager vs large imager, the physical size of the light sensing device itself. It’s a whole discipline in and of itself. Or there’s a limitation on the actual bandwidth itself, like a USB 2.0 interface. A JPEG format may be used because it’s cheaper to put an encoder on board than to have a fatter pipe to transfer the bits. Yes, there are a lot of factors in determining the best color space, a lot of them are related to the physical constraints of the imager and bandwidth restrictions. The ‘Research’ is for implementing CUDA code for image/video processing and possible solutions to vision processing, exploration or novel tasks. I believe that the intent by antmicro is to deliver a hardware platform for building vision enabled embedded systems and applications (which is the Development part of R&D). In most cases, the type of imager inside the camera makes data packing simpler in a given format, so that’s what manufacturers tend to deliver ‘natively’. The format gives you the map for dealing with the image. It’s the same image basically, but just stored in a particular way. It’s similar to photo images that are in a compressed format like JPEG or PNG. The main idea is that you have an image, and the image is in a given format whether it is YUV, RGB, Bayer and so on. You’ll probably hear about Bayer and RGB in a similar context, and conversion between the different formats in terms like “Bayer conversion to YUV 4:2:0”. There are a wide variety of formats that cameras/imagers deliver, YUV (sometimes called YCrCb) simply defines the structure of the digital data being delivered. YUV in this context means the format of the digital stream being delivered by the camera. ![]()
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