NeRF, or neural radiation field AI (artificial intelligence) models, is changing the 3D game.
NeRF models can generate a 3D scene from a sparse collection of 2D images of that scene. At the heart of NeRF models is a neural network that can fill in the gaps and correct for human errors. This process is often called volume rendering or reverse rendering because it reconstructs the original 3D scene from which the 2D images were rendered or captured.
NeRF is much more flexible than photogrammetry, which requires many more photos with a precise amount of overlap between them; uninterrupted coverage of all angles of the 3D scene; and perfect lighting conditions. In the end, you still receive sub-par results if the object moved or only a few of the frames were off, producing errors that must be manually corrected or the whole process restarted. On the other hand, NeRF uses the power of AI to interpolate more efficiently, greatly reducing the need for a perfect set of input images.
NeRF application areas
NeRF is a tool that revolutionizes the 3D creation pipeline. Whenever there is a need to translate a real-world object into a digital 3D copy or recreate 3D scenes from limited input data, NeRF can step in.
This has obvious applications in entertainment, media and marketing. As the world shifts to 3D with the coming proliferation of extended reality (XR) headsets, metaverse-like worlds, and augmented reality (AR) features, the primary content development bottleneck to enrich these digital worlds is the 3D creation pipeline.
NeRF opens this bottleneck. Instead of needing a team of 3D artists or an expensive photogrammetry setup, all you need to use NeRF is a smartphone with a camera. Companies like NVIDIA and Luma Labs offer apps to get started right away. Results like this, this and this are amazing. With NeRF, marketers can use real-world objects and spaces in promotional materials. Game designers can quickly populate their world with realistic 3D assets.
NeRF also has an impact on e-commerce. More and more e-commerce sites offer visualization features or 3D previews. If it makes sense for larger companies to invest in modeling high-quality assets for these insights, what about smaller sellers on Amazon? What about second-hand marketplaces like eBay? To stay competitive, even low-budget vendors can use NeRF to integrate their real-world objects, new or used, expensive or not, into product visualization applications.
NeRF also has uses in various other fields, from engineering to architecture to design. NeRF makes it easy for professionals to communicate their work – whether it’s a physical product, a new residential development, or a packaging design idea – to potential customers and internal team members. . NeRF allows members of all sections of the organization to easily create digital twins of factories, housing developments or product iterations.
NeRF is key to building the AR cloud
AR is one of the most important uses of NeRF. As consumer AR headsets proliferate in the coming years, more and more of the physical world will need to be represented in a format that digital technologies can understand. This underpins the idea of cloud AR, or a digital reconstruction of the physical world through point clouds, metadata, geolocation beacons, and IoT integration.
Building this mirror world is the first step to unlocking the power of augmented reality. For digital content like AR overlays, avatars, or animations to meaningfully interact with our physical world, it must understand its topology. Google Maps has made great strides in this area and will likely rely more on NeRF in its pipeline to create street views and 3D recreations. Niantic, the company behind popular AR app Pokémon Go, recently released its Lightship VPS tool designed for just that purpose: allowing users to scan real 3D scenes and use them in AR apps. NeRF would make these processes much faster and more accurate. If you want to try using NeRF, the tools from Luma Labs or the Instant NeRF tool from NVIDIA are good places to start.
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