PERFORMANCE ANALYSIS OF A LOW-COST TRIANGULATION-BASED 3D CAMERA: MICROSOFT KINECT SYSTEM
Bibliographic record
Abstract
Abstract. Recent technological advancements have made active imaging sensors popular for 3D modelling and motion tracking. The 3D coordinates of signalised targets are traditionally estimated by matching conjugate points in overlapping images. Current 3D cameras can acquire point clouds at video frame rates from a single exposure station. In the area of 3D cameras, Microsoft and PrimeSense have collaborated and developed an active 3D camera based on the triangulation principle, known as the Kinect system. This off-the-shelf system costs less than $150 USD and has drawn a lot of attention from the robotics, computer vision, and photogrammetry disciplines. In this paper, the prospect of using the Kinect system for precise engineering applications was evaluated. The geometric quality of the Kinect system as a function of the scene (i.e. variation of depth, ambient light conditions, incidence angle, and object reflectivity) and the sensor (i.e. warm-up time and distance averaging) were analysed quantitatively. This system's potential in human body measurements was tested against a laser scanner and 3D range camera. A new calibration model for simultaneously determining the exterior orientation parameters, interior orientation parameters, boresight angles, leverarm, and object space features parameters was developed and the effectiveness of this calibration approach was explored.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".