High-Definition 5MP Stereo Vision Sensing for Robotics
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| author | Jiang, Leaf Holzel, Matthew Kaplan, Bernhard Liu, Hsiou-Yuan Paul, Sabyasachi Rankin, Karen Swierczynski, Piotr |
| author_facet | Jiang, Leaf Holzel, Matthew Kaplan, Bernhard Liu, Hsiou-Yuan Paul, Sabyasachi Rankin, Karen Swierczynski, Piotr |
| contents | High-resolution (5MP+) stereo vision systems are essential for advancing robotic capabilities, enabling operation over longer ranges and generating significantly denser and accurate 3D point clouds. However, realizing the full potential of high-angular-resolution sensors requires a commensurately higher level of calibration accuracy and faster processing -- requirements often unmet by conventional methods. This study addresses that critical gap by processing 5MP camera imagery using a novel, advanced frame-to-frame calibration and stereo matching methodology designed to achieve both high accuracy and speed. Furthermore, we introduce a new approach to evaluate real-time performance by comparing real-time disparity maps with ground-truth disparity maps derived from more computationally intensive stereo matching algorithms. Crucially, the research demonstrates that high-pixel-count cameras yield high-quality point clouds only through the implementation of high-accuracy calibration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22445 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-Definition 5MP Stereo Vision Sensing for Robotics Jiang, Leaf Holzel, Matthew Kaplan, Bernhard Liu, Hsiou-Yuan Paul, Sabyasachi Rankin, Karen Swierczynski, Piotr Robotics Computer Vision and Pattern Recognition High-resolution (5MP+) stereo vision systems are essential for advancing robotic capabilities, enabling operation over longer ranges and generating significantly denser and accurate 3D point clouds. However, realizing the full potential of high-angular-resolution sensors requires a commensurately higher level of calibration accuracy and faster processing -- requirements often unmet by conventional methods. This study addresses that critical gap by processing 5MP camera imagery using a novel, advanced frame-to-frame calibration and stereo matching methodology designed to achieve both high accuracy and speed. Furthermore, we introduce a new approach to evaluate real-time performance by comparing real-time disparity maps with ground-truth disparity maps derived from more computationally intensive stereo matching algorithms. Crucially, the research demonstrates that high-pixel-count cameras yield high-quality point clouds only through the implementation of high-accuracy calibration. |
| title | High-Definition 5MP Stereo Vision Sensing for Robotics |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.22445 |