8 DAILY ICCV Thursday it can have a lot of impact. Let Yingsi explain: “If I'm capturing the scene in front of the car and there's a pedestrian walking by the cameras - any conventional camera is going to auto focus to that pedestrian. But then you lose focus to the street behind, like the far street and cars. But that's not desirable because you would want to know what's happening at all time.” Also in microcopy, if you want to capture different layers of a thick tissue, you can image the multiple depth simultaneously. You need post processing and that is time consuming. With this technology, you can have an arbitrary depth of field, arbitrary shape for the focal plane, which means you can image things at different depth at the same time. Yingsi wants to add one more key point: “With our spatially varying focusing framework, any type of autofocus algorithms can be readapted to a spatially varying way to the spatially varying framework. So we show examples of contrast detection autofocus (CDAF) and phase-detection autofocus (PDAF). But for follow up research, you can go beyond that. You don't have to stick to these two kinds of autofocus algorithms, although they are the mainstream today. You can use depth from defocus to produce the depth map. That's one way. And there are also other kinds of contrast detection autofocus algorithms like Best Paper Hon. Mention
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