Three dimensional high dynamic range veillance for 3D range-sensing cameras
Bibliographic record
Abstract
This paper presents the invention and implementation of 3D (Three Dimensional) HDR (High Dynamic Range) sensing, along with examples. We propose a method of 3D HDR veillance (sensing, computer vision, video capture, or the like) by integrating tonal and spatial information obtained from multiple HDR exposures for use in conjunction with one or more 3D cameras. In one embodiment, we construct a 3D HDR camera from multiple 3D cameras such as Kinect sensors. In this embodiment the 3D cameras are arranged in a fixed array, such that the geometric relationships between them remain constant over time. Only a single camera calibration step is required at the initial time of assembling and fixing the cameras into the array. Preferably the cameras either view from the same position through beam splitters, or are fixed close to one another, so that they capture approximately the same subject matter. The system is designed so the cameras each capture a differently exposed image or video of approximately the same subject matter. In one embodiment, two Kinect cameras are attached together facing in the same direction, with an ND (Neutral Density) filter over one of them, so as to obtain a darker exposure. The dark and light exposures are combined to obtain more accurate 3D sensing in high contrast scenes. In another embodiment, a single 3D camera is exposure-sequenced (alternating light and dark exposures). 3D HDR might, more generally, be incorporated into existing 3D cameras, resulting in a new kind of 3D sensor that can work in nearly any environment, including high contrast scenes such as outdoor scenes, or scenes where a bright light is shining directly into the sensor.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".