RECORD
3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint
Computer Vision and Pattern Recognition · Submitted 18 Jun 2020
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera in comparison with existing state-of-the-art methods.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Pipe inspection is a critical task for many industries and infrastructure of a city. The 3D information of a pipe can be used for revealing the deformation of the pipe surface and position of the camera during the inspection. In this paper, we propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera. Our work extends a state-of-the-art incremental Structure-from-Motion (SfM) method to incorporate prior constraints given by the target shape into bundle adjustment (BA). Using this constraint, we can minimize the scale-drift that is the general problem in SfM. Moreover, our method can reconstruct a pipe network composed of multiple parts including straight pipes, elbows, and tees. In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera in comparison with existing state-of-the-art methods.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Pipe inspection is a critical task for many industries and infrastructure of a city. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | The 3D information of a pipe can be used for revealing the deformation of the pipe surface and position of the camera during the inspection. | background 0.98 | 0.10 | 0.04 | asserted 0.88 |
| s2 | In this paper, we propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera. | contribution 0.91 | 0.87 | 0.07 | asserted 0.99 |
| s3 | Our work extends a state-of-the-art incremental Structure-from-Motion (SfM) method to incorporate prior constraints given by the target shape into bundle adjustment (BA). | contribution 0.53 | 0.87 | 0.08 | asserted 0.99 |
| s4 | Using this constraint, we can minimize the scale-drift that is the general problem in SfM. | interpretation 0.32 | 0.63 | 0.09 | asserted 0.79 |
| s5 | Moreover, our method can reconstruct a pipe network composed of multiple parts including straight pipes, elbows, and tees. | contribution 0.96 | 0.87 | 0.09 | asserted 0.83 |
| s6 | In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera in comparison with existing state-of-the-art methods. | result 0.99 | 0.95 | 0.84 | asserted 1.00 |
What was examined, and what was not
- arXiv listing abstract v2
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Pipe inspection is a critical task for many industries and infrastructure of a city. The 3D information of a pipe can be used for revealing the deformation of the pipe surface and position of the camera during the inspection. In this paper, we propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera. Our work extends a state-of-the-art incremental Structure-from-Motion (SfM) method to incorporate prior constraints given by the target shape into bundle adjustment (BA). Using this constraint, we can minimize the scale-drift that is the general problem in SfM. Moreover, our method can reconstruct a pipe network composed of multiple parts including straight pipes, elbows, and tees. In the experiments, we show that the proposed system enables more accurate and robust pipe mapping from a monocular camera in comparison with existing state-of-the-art methods.
Human review
How the literature uses it
What this paper does with the arXiv papers it cites
What it cites
Cites as background 1 paper
-
Another approach is to compensate scale information via pre-trained deep architectures that estimate an absolute scale depth [ref] and/or relative motion between the frames [ref] .
BA-Net: Dense Bundle Adjustment Network · Vision-based 3D reconstruction · 0.89
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.