Physical-Field-Bridged Material Parameter Inference from Mul
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: 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 deform…
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · simulations: 5000 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · object shapes: 100 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · loading modes: 5 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · stress-hotspot recall: 0.0762 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · SSIM: 0.8676 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · seconds: 30 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · minutes: 15 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Report timeline
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- arXiv Robotics — research abstractsPhysical-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.
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