Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos
Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos
This research presents a single-pass framework for estimating simulation-ready material parameters from visual observations of deformable objects. The method uses dense physical responses to connect multi-view RGB deformation videos with constitutive-type classification and continuous parameter regression. It achieves the best median errors on continuous parameters and improves stress-hotspot recall, while also demonstrating fast inference and future-state prediction in both simulated and real-world settings.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
simulations
5,000
object shapes
100
loading modes
5
stress-hotspot recall
0.0762–0.5121
SSIM
0.8676
seconds
≈30 seconds
View original evidence
inference and future-state prediction take approximately 30 seconds
Open source S5minutes
15–120 minutes
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
We construct DeformFieldBench, a 3DGS MPM learning environment comprising 5,000 simulations across 100 object shapes and five mechanically informative loading modes, with dense pixel-aligned physical supervision. Our method achieves the best median errors on all four continuous parameters, improves stress-hotspot recall from 0.0762 to 0.5121, and attains 0.8676 SSIM/36.82 dB PSNR in same-simulator replay on unseen ob
Open source S4
jects. Under a protocol-matched GSO evaluation, inference and future-state prediction take approximately 30 seconds, compared with 15-120 minutes for per-scene optimization. The method also yields measurable replay agreement on five real-world sequences. These results support dense physical fields as an effective bridge for fast material inference, while cross-simulator and real-world tests reveal remaining domain an
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org