A vison-based system for mapping the inside of a pipe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Underground pipes constitute the backbone of the infrastructure of a country. Dirty, broken, or clogged pipes have direct implications on the health hazards of humans. It is therefore no surprise that fault assessment of pipes is an important topic, which has received considerable attention in the past. While most pipe analysis systems rely on active sensors such as laser or sonar, the use of passive vision sensors has advantages in terms of cost and safety. This paper presents an automated 3D pipe reconstruction system using a single monocular camera as the only sensor. The contribution of our work is threefold. Firstly, the paper analyzes the implications of different environmental conditions on the result of the 3D reconstruction. Issues like different texture, diameter size, and lighting conditions are addressed. Secondly, while previous vision-based techniques use a special type of fisheye camera to perform the reconstruction, the method presented here is implemented using a regular off-the-shelf camera. Thirdly and finally, the 3D reconstruction system is the first to be able to detect and localize obstructions inside a pipe. Experiments are performed inside real pipes and results prove the success of our techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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