Abstract | ||
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Stereo matching has attracted much attention in recent years. Traditional methods can quickly generate a disparity result, but the accuracy is low. On the contrary, methods based on neural networks can achieve a high accuracy level, but they are difficult to reach the real-time level. Therefore, this paper presents MCDRNet, which combines traditional methods with neural networks to achieve real-time and accurate stereo matching results. Concretely, our network first generates a rough disparity map based on the traditional ADCensus algorithm. Then we design a novel Multi-Scale Cascade Network to refine the disparity map from coarse to fine. We evaluate our best-trained model on the KITTI official website. The results show that our network is much faster than most current top-performing methods(31xthan CSPN, 56xthan GANet, etc.). Meanwhile, it is more accurate than traditional stereo methods(SGM, SPS-St) and other fast 2D convolution networks(Fast DS-CS, DispNetC, etc.), demonstrating the rationalities and feasibilities of our method. |
Year | DOI | Venue |
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2021 | 10.1109/ICASSP39728.2021.9414923 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Computer vision, Stereo matching, Depth estimation, Traditional method, Neural network | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Xiaogang Jia | 1 | 0 | 1.69 |
Wei Chen | 2 | 1711 | 246.70 |
Zhengfa Liang | 3 | 27 | 7.58 |
Xin Luo | 4 | 2 | 3.76 |
Mingfei Wu | 5 | 13 | 3.25 |
Yusong Tan | 6 | 1 | 2.72 |
Libo Huang | 7 | 82 | 25.47 |