Linear-PoseNet: A Real-Time Camera Pose Estimation System Using Linear Regression and Principal Component Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Neural networks-based camera pose estimation systems rely on fine tuning very large networks to regress the camera position and orientation with very complex training procedure. In this paper, we explore the following question: do we need to fine tune and train such complex networks to reach the desired accuracy? We show that we can reach comparable or better accuracy for the single image indoor localization systems with using only one layer of ridge regression and pretrained features of ResNet-50 architecture with training time less than a second on CPU instead of hours of GPU training needed by the state of the art. For outdoor scenes, we show that using only 3 fully connected layers on top of pretrained ResNet50 features without fine-tuning can perform well compared to the state of the art with only minutes of training. For more complexity reduction, we show that downsampling the pretrained ResNet-50 features by more than 10 times using principal component analysis (PCA) has a little effect on the performance but can save both training time and storage space.
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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) | 0.000 | 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