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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-2026104249-A1, on 16 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000000387

Systems and Methods for Training a Neural Network for 3D Object Reconstruction

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Sensors, Controls & Machine Vision › Position and Presence Sensing
Original source
US-2026104249-A1, the United States Patent and Trademark Office, 16 April 2026
Document SHA-256
0366fc23b549c2c3ecef352cf51ec578ba6aec91ce5873a6423e232e17f210ca

Abstract

In accordance with some embodiments, systems and methods are provided for training a neural network using simulated data so that the neural network can determine correspondence between a projection pattern and an image of the projection pattern shone onto the surface of an object. The trained neural network can be used to output a correspondence between respective pixels in an image of a real object with the projection pattern projected thereon and coordinates of the projection pattern. The method can further include, using the correspondence between respective pixels in the image and coordinates of the projection pattern, reconstructing a shape of the surface of the real object.

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Citation

OpenTechnical Publication OT-2026-000000387, “Systems and Methods for Training a Neural Network for 3D Object Reconstruction,” published September 15, 2026 at 19:02:49 UTC, https://opentechnical.org/p/OT-2026-000000387.

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