Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect
Bibliographic record
Abstract
The Kinect system is arguably the most popular 3-D camera technology currently on the market. Its application domain is vast and has been deployed in scenarios where accurate geometric measurements are needed. Regarding the PrimeSense technology, a limited amount of work has been devoted to calibrating the Kinect, especially the depth data. The Kinect is, however, inevitably prone to distortions, as independently confirmed by numerous users. An effective method for improving the quality of the Kinect system is by modeling the sensor's systematic errors using bundle adjustment. In this paper, a method for modeling the intrinsic and extrinsic parameters of the infrared and colour cameras, and more importantly the distortions in the depth image, is presented. Through an integrated marker-and feature-based self-calibration, two Kinects were calibrated. A novel approach for modeling the depth systematic errors as a function of lens distortion and relative orientation parameters is shown to be effective. The results show improvements in geometric accuracy up to 53% compared with uncalibrated point clouds captured using the popular software RGBDemo. Systematic depth discontinuities were also reduced and in the check-plane analysis the noise of the Kinect point cloud was reduced by 17%.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".