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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-12561830-B2, on 24 February 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-2026-000002581

Neural implicit scattering functions for inverse parameter estimation and dynamics modeling of multi-object interactions

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Classification
Robotics & Automation › Robot Architectures
Inventors
CAI YANCHENG, GAIDON ADRIEN DAVID, LIU KATHERINE, LI YUNZHU, TIAN STEPHEN, WU JIAJUN, Yu hong-xing, ZAKHAROV SERGEY
Assignee
TOYOTA JIDOSSHA KK, TOYOTA RES INST INC, UNIV LELAND STANFORD JUNIOR
Original source
US-12561830-B2, the United States Patent and Trademark Office, 24 February 2026
Document SHA-256
f8322550952bcaf3571ea237dd79aeea968bf7c7ccb056ef9d95a5e51319e6a3

Abstract

A method for dynamic modeling and manipulation of multi-object scenes is described. The method includes using object-centric neural implicit scattering functions (OSFs) as object representations in a model-predictive control (MPC) framework for the multi-object scenes. The method also includes modeling a per-object light transport to enable compositional scene re-rendering under object rearrangement and varying lighting conditions. The method further includes applying inverse parameter estimation and graph neural network (GNN) dynamics models to estimate initial object poses and a light position in the multi-object scene. The method also includes manipulating an object perceived in the multi-object scene according to the applying of the inverse parameter estimation and the GNN dynamics models.

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OpenTechnical Publication OT-2026-000002581, “Neural implicit scattering functions for inverse parameter estimation and dynamics modeling of multi-object interactions,” published September 15, 2026 at 19:13:59 UTC, https://opentechnical.org/p/OT-2026-000002581.

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