Details
| Original language | English |
|---|---|
| Article number | 6726 |
| Pages (from-to) | 1-23 |
| Number of pages | 23 |
| Journal | Sensors (Switzerland) |
| Volume | 20 |
| Issue number | 23 |
| Publication status | Published - 25 Nov 2020 |
Abstract
This paper presents an active wide-baseline triple-camera measurement system designed especially for 3D modeling in general outdoor environments, as well as a novel parallel surface refinement algorithm within the multi-view stereo (MVS) framework. Firstly, the pre-processing module converts the synchronized raw triple images from one single-shot acquisition of our setup to aligned RGB-Depth frames, which are then used for camera pose estimation using iterative closest point (ICP) and RANSAC perspective-n-point (PnP) approaches. Afterwards, an efficient dense reconstruction method, mostly implemented on the GPU in a grid manner, takes the raw depth data as input and optimizes the per-pixel depth values based on the multi-view photographic evidence, surface curvature and depth priors. Through a basic fusion scheme, an accurate and complete 3D model can be obtained from these enhanced depth maps. For a comprehensive test, the proposed MVS implementation is evaluated on benchmark and synthetic datasets, and a real-world reconstruction experiment is also conducted using our measurement system in an outdoor scenario. The results demonstrate that (1) our MVS method achieves very competitive performance in terms of modeling accuracy, surface completeness and noise reduction, given an input coarse geometry; and (2) despite some limitations, our triple-camera setup in combination with the proposed reconstruction routine, can be applied to some practical 3D modeling tasks operated in outdoor environments where conventional stereo or depth senors would normally suffer.
Keywords
- Active triple-camera measurement system, Camera pose estimation, Parallel surface refinement
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Computer Science(all)
- Information Systems
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
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In: Sensors (Switzerland), Vol. 20, No. 23, 6726, 25.11.2020, p. 1-23.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Scale-aware multi-view reconstruction using an active triple-camera system
AU - Luo, Hang
AU - Pape, Christian
AU - Reithmeier, Eduard
N1 - Funding information: This research was funded by China Scholarship Council.
PY - 2020/11/25
Y1 - 2020/11/25
N2 - This paper presents an active wide-baseline triple-camera measurement system designed especially for 3D modeling in general outdoor environments, as well as a novel parallel surface refinement algorithm within the multi-view stereo (MVS) framework. Firstly, the pre-processing module converts the synchronized raw triple images from one single-shot acquisition of our setup to aligned RGB-Depth frames, which are then used for camera pose estimation using iterative closest point (ICP) and RANSAC perspective-n-point (PnP) approaches. Afterwards, an efficient dense reconstruction method, mostly implemented on the GPU in a grid manner, takes the raw depth data as input and optimizes the per-pixel depth values based on the multi-view photographic evidence, surface curvature and depth priors. Through a basic fusion scheme, an accurate and complete 3D model can be obtained from these enhanced depth maps. For a comprehensive test, the proposed MVS implementation is evaluated on benchmark and synthetic datasets, and a real-world reconstruction experiment is also conducted using our measurement system in an outdoor scenario. The results demonstrate that (1) our MVS method achieves very competitive performance in terms of modeling accuracy, surface completeness and noise reduction, given an input coarse geometry; and (2) despite some limitations, our triple-camera setup in combination with the proposed reconstruction routine, can be applied to some practical 3D modeling tasks operated in outdoor environments where conventional stereo or depth senors would normally suffer.
AB - This paper presents an active wide-baseline triple-camera measurement system designed especially for 3D modeling in general outdoor environments, as well as a novel parallel surface refinement algorithm within the multi-view stereo (MVS) framework. Firstly, the pre-processing module converts the synchronized raw triple images from one single-shot acquisition of our setup to aligned RGB-Depth frames, which are then used for camera pose estimation using iterative closest point (ICP) and RANSAC perspective-n-point (PnP) approaches. Afterwards, an efficient dense reconstruction method, mostly implemented on the GPU in a grid manner, takes the raw depth data as input and optimizes the per-pixel depth values based on the multi-view photographic evidence, surface curvature and depth priors. Through a basic fusion scheme, an accurate and complete 3D model can be obtained from these enhanced depth maps. For a comprehensive test, the proposed MVS implementation is evaluated on benchmark and synthetic datasets, and a real-world reconstruction experiment is also conducted using our measurement system in an outdoor scenario. The results demonstrate that (1) our MVS method achieves very competitive performance in terms of modeling accuracy, surface completeness and noise reduction, given an input coarse geometry; and (2) despite some limitations, our triple-camera setup in combination with the proposed reconstruction routine, can be applied to some practical 3D modeling tasks operated in outdoor environments where conventional stereo or depth senors would normally suffer.
KW - Active triple-camera measurement system
KW - Camera pose estimation
KW - Parallel surface refinement
UR - http://www.scopus.com/inward/record.url?scp=85096540453&partnerID=8YFLogxK
U2 - 10.3390/s20236726
DO - 10.3390/s20236726
M3 - Article
C2 - 33255532
AN - SCOPUS:85096540453
VL - 20
SP - 1
EP - 23
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
SN - 1424-8220
IS - 23
M1 - 6726
ER -