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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2212.00476 |
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| _version_ | 1866918230503718912 |
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| author | Chang, Qiong Zha, Aolong Wang, Weimin Liu, Xin Onishi, Masaki Lei, Lei Er, Meng Joo Maruyama, Tsutomu |
| author_facet | Chang, Qiong Zha, Aolong Wang, Weimin Liu, Xin Onishi, Masaki Lei, Lei Er, Meng Joo Maruyama, Tsutomu |
| contents | Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel's computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1,280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed. Source Code: https://github.com/changqiong/Z2ZNCC.git |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_00476 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Efficient stereo matching on embedded GPUs with zero-means cross correlation Chang, Qiong Zha, Aolong Wang, Weimin Liu, Xin Onishi, Masaki Lei, Lei Er, Meng Joo Maruyama, Tsutomu Computer Vision and Pattern Recognition Hardware Architecture Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel's computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1,280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed. Source Code: https://github.com/changqiong/Z2ZNCC.git |
| title | Efficient stereo matching on embedded GPUs with zero-means cross correlation |
| topic | Computer Vision and Pattern Recognition Hardware Architecture |
| url | https://arxiv.org/abs/2212.00476 |