Extending the Range of Depth Cameras using Linear Perspective for Mobile Robot Applications
Bibliographic record
Abstract
Reliable depth sensing is essential in robotics for both basic and advanced robot operations. Depth cameras capture depth data that can be used by a robot’s vision system, but the quality of the data is limited. Many depth estimation and completion algorithms have been introduced to process camera data and predict depth in a scene, but extending camera range is a little explored problem. This thesis presents a geometry-based method that applies a Manhattan constraint and regresses onto sparse depth input to interpolate and extrapolate lines in the scene in order to extend range. To evaluate the proposed approach, a long-range RGBD dataset with corresponding LiDAR ground truth is presented. Experiments demonstrate that the proposed method successfully interpolates and extrapolates detected 3D lines in Manhattan scenes given sparse depth data within a few centimeters of error, providing depth information in parts of the scene missing input depth data from the sensor. The proposed approach performs comparably to a baseline method in interpolating depth and outperforms it in extrapolation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".