Summary of The Sampling-gaussian For Stereo Matching, by Baiyu Pan and Jichao Jiao and Bowen Yao and Jianxin Pang and Jun Cheng
The Sampling-Gaussian for stereo matching
by Baiyu Pan, jichao jiao, Bowen Yao, Jianxin Pang, Jun Cheng
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel supervision method for stereo matching, called Sampling-Gaussian, which addresses the limitations of previous distribution-based methods. The soft-argmax operation, widely used in neural network-based stereo matching, can lead to multimodal outputs due to the absence of explicit constraints on the probability distribution. To improve the accuracy and efficiency of stereo matching networks, the authors analyze previous methods and develop a combined loss function that incorporates L1 loss and cosine similarity loss. They also introduce bilinear interpolation for upsampling cost volumes. The proposed method can be easily applied to any soft-argmax-based stereo matching method without sacrificing efficiency. Experimental results demonstrate the superior performance of Sampling-Gaussian, achieving better accuracy on five baseline methods and two datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can match images from different angles, which is important for applications like self-driving cars. The authors propose a new way to train computer networks to do this job well. They studied what didn’t work in the past and created a new approach that combines different ways of measuring how good the network is doing. This helps the network be more accurate without using too much processing power. The results show that their method works better than previous methods on several types of images. |
Keywords
» Artificial intelligence » Cosine similarity » Loss function » Neural network » Probability
