Camera images and ML-extracted primitives generate accurate 2D room plans without depth sensors or time-consuming manual input.
Adapted Gaussian splats replace redundant point sets to cut 3D streaming data while preserving visual fidelity for rendering.
A RaaS workflow converts industrial equipment 3D model data into metaverse-ready renderings with less manual format handling.
Generative image objects with alpha transparency can be edited, moved, and regenerated independently instead of reworking the full image.
AI turns a 2D image into a manipulable 3D virtual object, giving users multiple views and real-time rotation in shared sessions.
Machine learning separates floor and elevation sheets, extracts contours, and aligns them with 3D maps to locate buildings faster.
Grouped-ray BVH traversal reorders child node visits to cut memory bandwidth use and speed ray tracing in complex scenes.
Precomputed mesh groups and traversal likelihood metrics cut acceleration-structure cost and memory while keeping ray testing efficient.
Loss-weighted image sampling focuses splat training on high-loss views to cut 3D model file size and wasted iterations.
A conservative low-resolution BVH cuts ray traversal cost by culling nodes early while preserving access to full-resolution bounding data.