A virtual-environment-based object construction method uses a camera to capture 3D information of an object for generating feature point clouds.
Ancillary format images preserve raw data integrity, reducing storage and bandwidth demands while maintaining image quality.
Multiple stationary range cameras on a single tower capture depth images of a rotating subject, eliminating complex moving rails and wired connections.
A network uses 3D-OCR and GeoReS modules to align orientation and detect symmetry for category-level pose estimation.
Correction unit compensates for apparatus shake and lens shifts between feature points, preserving shape data accuracy despite dynamic capture conditions.
Collaborative 3D modeling system enables sketch-based shape generation and interactive manipulation across desktop and mobile interfaces.
A 3D space view display system generates interactive virtual apartment layouts using VR technology and neural networks.
Structured light and time-of-flight sensors create a 3D body model that automates region of interest determination, reducing manual operator intervention.
Segmented source arrays reduce capture time without increasing system bulk, resolving the trade-off between speed and complexity.
Converting 2D photo coordinates into 3D model viewpoints enables automated visual comparison, eliminating time loss from physical on-site inspections.
A vehicle system generates virtual images from front camera data to visualize obscured areas.
Multi-wavelength light tracing determines surface coordinates by segmenting the optical path, overcoming distortion from refractive highlights.
A deep learning network predicts pressure maps using structured 3D models generated from 2D photographs and object parameters.