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

arXiv: https://arxiv.org/abs/2608.07726 · HTML: https://mlsanity.com/paper/2608.07726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 24 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 22 of 22)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics](https://mlsanity.com/paper/2311.12198/) (2023)
- [ABO: Dataset and Benchmarks for Real-World 3D Object Understanding](https://mlsanity.com/paper/2110.06199/) (2021)
- [PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation](https://mlsanity.com/paper/2404.13026/) (2024)
- [PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation](https://mlsanity.com/paper/2409.18964/) (2024)
- [Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion](https://mlsanity.com/paper/2406.04338/) (2024)
- [PhysGen3D: Crafting a Miniature Interactive World from a Single Image](https://mlsanity.com/paper/2503.20746/) (2025)
- [Physical Property Understanding from Language-Embedded Feature Fields](https://mlsanity.com/paper/2404.04242/) (2024)
- [PhysX-3D: Physical-Grounded 3D Asset Generation](https://mlsanity.com/paper/2507.12465/) (2025)
- [DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors](https://mlsanity.com/paper/2406.01476/) (2024)
- [Efficient Physics Simulation for 3D Scenes via MLLM-Guided Gaussian Splatting](https://mlsanity.com/paper/2411.12789/) (2024)
- [HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar](https://mlsanity.com/paper/2210.12564/) (2022)
- [PUGS: Zero-shot Physical Understanding with Gaussian Splatting](https://mlsanity.com/paper/2502.12231/) (2025)
- [Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels](https://mlsanity.com/paper/2508.17437/) (2025)
- [Phys4DGen: Physics-Compliant 4D Generation with Multi-Material Composition Perception](https://mlsanity.com/paper/2411.16800/) (2024)
- [Diffusion Model is a Good Pose Estimator from 3D RF-Vision](https://mlsanity.com/paper/2403.16198/) (2024)
- [VoMP: Predicting Volumetric Mechanical Property Fields](https://mlsanity.com/paper/2510.22975/) (2025)
- [RFPose-OT: RF-Based 3D Human Pose Estimation via Optimal Transport Theory](https://mlsanity.com/paper/2301.13013/) (2022)
- [Improving Real-Time Omnidirectional 3D Multi-Person Human Pose Estimation with People Matching and Unsupervised 2D-3D Lifting](https://mlsanity.com/paper/2403.09437/) (2024)
- [The Field-based Model: A New Perspective on RF-based Material Sensing](https://mlsanity.com/paper/2412.05640/) (2024)
- [Adaptive Volumetric Mechanical Property Fields Invariant to Resolution](https://mlsanity.com/paper/2606.18231/) (2026)

## Abstract (arXiv)

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. On GVM, ViWi improves over the prior state of the art on four of six per-voxel metrics, while its vision-only variant improves all reported mass-estimation metrics on ABO-500. 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.