WiCaliper: Simultaneous Material and 3D Size Sensing for Everyday Objects Using WiFi
Bibliographic record
Abstract
Alongside the ongoing standardization efforts for WiFi sensing, WiFi has emerged as a leading technology for Integrated Sensing and Communications (ISAC) with numerous sensing applications demonstrating its significant potentials. Material and size sensing, essential in quality control and digital twins, has drawn much interest. Yet,simultaneous material and 3D size sensingremains challenging, primarily due to the lack of suitable sensing models for objects at near-wavelength scales. This paper introduces WiCaliper, the first WiFi-based system addressing this problem. Its core innovation is DP-CSI, a novel sensing model that captures both diffraction and penetration effects to characterize the relationship between channel state information and the material, shape, and size of everyday 3D objects. To effectively solve for multiple object parameters, WiCaliper employs a two-step estimation process consisting of closed-form property function recovery and multi-view joint parameter optimization. Experimental evaluations show that it achieves 95% material classification accuracy and a 1.5 cm median error in 3D size sensing. This work advances ISAC theory by establishing principles for wavelength-scale 3D object sensing, paving the way for new sensing applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".