Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties

2026-08-07

AI Summary

Accurately estimating voxel-level mechanical properties—such as Young’s modulus, Poisson’s ratio, and density—from visual information alone is challenging, as visually similar objects may exhibit substantially different material compositions. To address this ambiguity, this work proposes the ViWi framework, which uniquely integrates an object-centric material slot mechanism with electromagnetic simulations in the WiFi frequency band, leveraging dielectric permittivity and electrical conductivity. By aggregating voxel-wise evidence from shared materials and fusing radio-frequency descriptors, ViWi introduces a global material prior that effectively disambiguates purely visual estimates. The proposed method outperforms state-of-the-art approaches on four out of six voxel-level mechanical property metrics, and even its vision-only variant achieves consistent improvements across all mass estimation benchmarks.

Abstract

Estimating volumetric mechanical properties, including Young's modulus, Poisson's ratio, and density at each voxel, is intrinsically ambiguous from vision alone, as visually similar objects may have substantially different material compositions and physical behavior. Existing approaches predict these properties independently across voxels, overlooking the piecewise-constant material structure of real objects and producing noisy or inconsistent estimates for voxels that share the same material, while lacking an explicit mechanism to resolve visual ambiguity. We introduce ViWi (Vision Meets WiFi), an object-centric framework for volumetric mechanical-property estimation. ViWi represents each object using a compact set of material slots that aggregate evidence from voxels with a shared material identity and produce coherent slot-level property predictions. To complement visual appearance, ViWi incorporates a compact RF descriptor generated through WiFi-band electromagnetic simulation using permittivity and conductivity. The RF descriptor conditions the material slots with global composition cues that may be unavailable from images, while visual features preserve voxel-level spatial localization. Across volumetric mechanical-property and mass-estimation benchmarks, ViWi improves over the prior state of the art on four of six per-voxel metrics, while its vision-only variant improves all mass-estimation metrics. These results demonstrate that combining object-centric material structure with complementary RF evidence enables more accurate and physically coherent volumetric property estimation beyond what is possible from visual appearance alone.

Problem

• volumetric mechanical properties
• visual ambiguity
• material composition
• Young's modulus
• Poisson's ratio

Innovation

• material slots
• volumetric mechanical properties
• WiFi-based RF descriptor
• physics-grounded estimation
• object-centric representation