Single User WiFi Structure from Motion in the Wild
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a novel motion estimation algorithm using WiFi networks and IMU sensor data in large uncontrolled environments, dubbed “WiFi Structure-from-Motion” (WiFi SfM). Given smartphone sensor data through day-to-day activities from a single user over a month, our WiFi SfM algorithm estimates smartphone motion tra-jectories and the structure of the environment represented as a WiFi radio map. The approach 1) establishes frame-to-frame correspondences based on WiFi fingerprints while exploiting our repetitive behavior patterns; 2) aligns trajectories via bundle adjustment; and 3) trains a self-supervised neural network to extract further motion constraints. We have col-lected 235 hours of smartphone data, spanning 38 days of daily activities in a university campus. Our experiments demonstrate the effectiveness of our approach over the competing methods with qualitative evaluations of the estimated motions and quantitative evaluations of indoor localization accuracy based on the reconstructed WiFi radio map. The WiFi SfM technology will potentially allow digital mapping companies to build better radio maps automatically by asking users to share WiFi/IMU sensor data in their daily activities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it