Details Involving Processing Steps TRIZ Cases

Neural networks turn multi-view video and depth data into non-riggable 3D face avatars with realistic gaze and expression while reducing bandwidth.

Multiple overlapping intraoral scans are globally registered to correct pose drift and reduce deformation in the final 3D image.

Semantic cues identify missing object surfaces and guide users to capture incomplete views for more complete 3D reconstruction.

Machine learning turns mobile vehicle images into 3D damage models, enabling faster, more consistent remote claim assessment.

Captured scene images are fused with neural rendering and partial 3D synthesis to enable fast photorealistic edits to lighting, background, and composition.

Ground-level image capture builds scaled 3D building models with labeled facade elements, accurate dimensions, and material cost estimates.

Separating transparent and opaque subject regions removes background color carryover, producing coherent virtual viewpoint images.

Guided smartphone facial imaging improves angle and lighting for dental CAD, avoiding costly 3D scanners and X-ray exposure.

Iterative face-model fitting and vertex refinement improve 3D avatar realism while preserving gaze, head orientation, and transmission efficiency.

3D body models enable virtual try-on, accurate apparel fit assessment, and shared shopping across phones, tablets, and computers.

Incremental 3D scans keep virtual store layouts and product displays accurate while reducing memory, processing load, and bandwidth.