From images to properties: a NeRF-driven framework for granular material parameter inversion

TL;DR

This paper presents a NeRF-driven framework that combines 3D reconstruction and simulation to accurately infer granular material properties, specifically the friction angle, from visual observations alone.

Contribution

It introduces a novel integration of NeRF and MPM simulation with Bayesian optimization for material property inversion from images.

Findings

Friction angle estimated within 2 degrees accuracy.

Effective inverse analysis using only visual data.

Framework applicable to real-world granular material characterization.

Abstract

We introduce a novel framework that integrates Neural Radiance Fields (NeRF) with Material Point Method (MPM) simulation to infer granular material properties from visual observations. Our approach begins by generating synthetic experimental data, simulating an plow interacting with sand. The experiment is rendered into realistic images as the photographic observations. These observations include multi-view images of the experiment's initial state and time-sequenced images from two fixed cameras. Using NeRF, we reconstruct the 3D geometry from the initial multi-view images, leveraging its capability to synthesize novel viewpoints and capture intricate surface details. The reconstructed geometry is then used to initialize material point positions for the MPM simulation, where the friction angle remains unknown. We render images of the simulation under the same camera setup and compare them to the observed images. By employing Bayesian optimization, we minimize the image loss to estimate the best-fitting friction angle. Our results demonstrate that friction angle can be estimated with an error within 2 degrees, highlighting the effectiveness of inverse analysis through purely visual observations. This approach offers a promising solution for characterizing granular materials in real-world scenarios where direct measurement is impractical or impossible.