These are:ĭeveloper resources including the 3D Tiles Next Reference Card, sample data, and the upcoming 3D Tiles Next Tech blog series. The state of the 3D Tiles Next ecosystem is currently:ĭraft 3D Tiles and glTF extensions ready for your feedback on GitHub and prototype implementations in your software. For example, transmission will enhance the visual fidelity of glass on windshields and buildings by representing the thin-surface transparency in a physically plausible way that absorbs, reflects, and transmits light. GlTF’s PBR Next initiative has brought together the world’s experts on PBR to advance glTF’s material representation from metal-roughness and specular-gloss to support a wide array of new visual effects such as clearcoat, transmission, and volumetric efforts. We are especially excited to apply this to 3D geospatial content, where imagery captured via satellites and drones is creating an explosion of textures at global scale. KTX 2.0 enables compressed textures for both transmission and runtime use across GPU vendors, enabling versatile optimizations that reduce memory, bandwidth, and power usage. In addition, 3D Tiles Next will continue to use glTF extensions such as KTX 2.0 and PBR Next materials. This will facilitate 3D Tiles better contributing to the glTF ecosystem and potentially a glTF Geospatial Profile. Since 3D Tiles Next no longer uses b3dm, i3dm, and pnts containers, everything that augments a glTF tile content will be a glTF extension, as shown in the examples above. This is a key enabler of the 3D Tiles ecosystem: a 3D Tiles runtime engine can stream 3D Tiles from any source. In both cases, the generated 3D tileset can be streamed by the same 3D Tiles runtime engine as long as the subdivision follows the spatial coherence rule: the content of child tiles need to be spatially inside the parent tile’s bounding volume. For example, a city model defined by extruded footprints and a model of the same city collected by LIDAR may have different subdivisions based on each model’s data density. The 3D Tiles approach allows the flexibility to create a wide array of spatial data structures so that many different 3D tiling pipelines can be developed to generate optimized 3D tilesets for the given input. This design is more general purpose than traditional 2D map tiling using global quadtrees. In 3D Tiles 1.0, spatial subdivision is explicitly defined in one or more tileset.json files defining the spatial data structure, i.e., each tile’s bounding volume, content, and child tiles, recursively to the leaf tiles. A model is divided into many streamable pieces called tiles, and then a hierarchy of combined low-detail tiles are created to enable cloud-based view-dependent streaming using a technique called out-of-core hierarchical level of detail. Not only does the spatial data structure of 3D Tiles Next optimize streaming for visualization, but it also optimizes large-scale simulation and analytics, where algorithms like ray tracing and nearest neighbor searches benefit from spatial subdivision.ģD Tiles scales to such massive models because of this spatial subdivision. Since 3D Tiles 1.0, we have seen increasing interest in running large-scale simulations with many agents in a real-world 3D model, and also in performing analytics, such as line of sight or RF propagation, in similar environments. 3D Tiles Next optimizes spatial subdivision, enabling fast spatial queries, faster analysis, and greater interoperability with GIS and simulation ecosystems.
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