18539157. NEURAL IMPLICIT SCATTERING FUNCTIONS FOR INVERSE PARAMETER ESTIMATION AND DYNAMICS MODELING OF MULTI-OBJECT INTERACTIONS simplified abstract (TOYOTA RESEARCH INSTITUTE, INC.)

NEURAL IMPLICIT SCATTERING FUNCTIONS FOR INVERSE PARAMETER ESTIMATION AND DYNAMICS MODELING OF MULTI-OBJECT INTERACTIONS

Organization Name

TOYOTA RESEARCH INSTITUTE, INC.

Inventor(s)

Stephen Tian of Stanford CA (US)

Yancheng Cai of Cambridge (GB)

Hong-Xing Yu of Stanford CA (US)

Sergey Zakharov of Stanford CA (US)

Katherine Liu of Mountain View CA (US)

Adrien David Gaidon of Mountain View CA (US)

Yunzhu Li of Stanford CA (US)

Jiajun Wu of Stanford CA (US)

NEURAL IMPLICIT SCATTERING FUNCTIONS FOR INVERSE PARAMETER ESTIMATION AND DYNAMICS MODELING OF MULTI-OBJECT INTERACTIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18539157 titled 'NEURAL IMPLICIT SCATTERING FUNCTIONS FOR INVERSE PARAMETER ESTIMATION AND DYNAMICS MODELING OF MULTI-OBJECT INTERACTIONS

The method described in the patent application involves dynamic modeling and manipulation of multi-object scenes using object-centric neural implicit scattering functions (OSFs) within a model-predictive control (MPC) framework. This allows for re-rendering scenes under object rearrangement and varying lighting conditions.

• Object-centric neural implicit scattering functions (OSFs) are used as object representations in a model-predictive control (MPC) framework for multi-object scenes.
• Per-object light transport modeling enables compositional scene re-rendering under object rearrangement and changing lighting conditions.
• Inverse parameter estimation and graph neural network (GNN) dynamics models are applied to estimate initial object poses and light positions in the scene.
• The method allows for manipulating objects in multi-object scenes based on the estimated parameters from the inverse parameter estimation and GNN dynamics models.

Potential Applications: - Computer graphics and animation - Virtual reality and augmented reality environments - Robotics and autonomous systems - Industrial automation and manufacturing processes

Problems Solved: - Efficient modeling and manipulation of multi-object scenes - Real-time re-rendering of scenes under changing conditions - Accurate estimation of object poses and lighting positions

Benefits: - Enhanced realism and flexibility in scene rendering - Improved control and manipulation of objects in dynamic environments - Increased efficiency in modeling complex scenes

Commercial Applications: Dynamic modeling and manipulation technology can be utilized in various industries such as entertainment, gaming, architecture, engineering, and e-commerce for realistic visualization, simulation, and interactive experiences.

Questions about the technology: 1. How does the use of object-centric neural implicit scattering functions improve scene modeling and manipulation? 2. What are the potential limitations or challenges in implementing this technology in real-world applications?

Frequently Updated Research: Researchers are constantly exploring advancements in neural network models, MPC frameworks, and inverse parameter estimation techniques to further enhance the capabilities of dynamic scene modeling and manipulation technologies.

Original Abstract Submitted

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.