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摘要:
针对基于CNN的立体匹配方法中特征提取难以较好学习全局和远程上下文信息的问题，提出一种基于Swin Transformer的立体匹配网络改进模型 (stereo matching net with swin transformer fusion, STransMNet)。分析了在立体匹配过程中，聚合局部和全局上下文信息的必要性和匹配特征的差异性。改进了特征提取模块，把基于CNN的方法替换为基于Transformer的Swin Transformer方法；并在Swin Transformer中加入多尺度特征融合模块，使得输出特征同时包含浅层和深层语义信息；通过提出特征差异化损失改进了损失函数，以增强模型对细节的注意力。最后，在多个公开数据集上与STTR-light模型进行了对比实验，误差(End-Point-Error, EPE) 和匹配错误率3 px error均有明显降低。
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关键词:
- 立体匹配 /
- Swin Transformer /
- 深度学习 /
- STransMNet
Abstract:Feature extraction in the CNN-based stereo matching models has the problem that it is difficult to learn global and long-range context information. To solve this problem, an improved model STransMNet stereo matching network based on the Swin Transformer is proposed in this paper. We analyze the necessity of the aggregated local and global context information. Then the difference in matching features during the stereo matching process is discussed. The feature extraction module is improved by replacing the CNN-based algorithm with the Transformer-based Swin Transformer algorithm to enhance the model's ability to capture remote context information. The multi-scale fusion module is added in Swin Transformer to make the output features contain shallow and deep semantic information. The loss function is improved by introducing the feature differentiation loss to enhance the model's attention to details. Finally, the comparative experiments with the STTR-light model are conducted on multiple public datasets, showing that the End-Point-Error (EPE) and the matching error rate of 3 px error are significantly reduced.
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Key words:
- stereo matching /
- Swin Transformer /
- deep learning /
- STransMNet
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图 2 (a) STransMNet网络结构；(b) 特征提取器的结构
Figure 2. (a) The network structure of STransMNet; (b) The structure of extractor
图 3 左图像上的像素特征之间的欧式距离。(a) 加入特征差异化损失；(b) 未加入特征差异化损失
Figure 3. Euclidean distance between the pixel features on the left image. (a) There is a feature differentiation loss; (b) No a feature differentiation loss
图 4 不同方法在Sceneflow数据集上的估计的视差图
Figure 4. Disparity map estimated by different methods on the Sceneflow datasets
图 5 不同方法在KITTI数据集上的估计的视差图
Figure 5. Disparity map estimated by different methods on the KITTI datasets
表 1 消融实验
Table 1. Ablation study
实验 基于Swin Transformer模块 相关运算 特征差异化损失 3 px error / % ↓ EPE ↓ Occ IOU ↑ 第1组 1.68 0.56 0.94 第2组 √ 1.36 0.48 0.96 第3组 √ √ 1.40 0.51 0.95 第4组 √ √ √ 1.03 0.42 0.97 
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表 2 不同的损失权重实验结果
Table 2. Experimental results of different loss weights
Ld1,r Ld1,f Lrr Lbe,f Ldiff 3 px error /%↓ EPE ↓ Occ IOU ↑ 0.2 0.2 0.2 0.2 0.2 0.85 0.41 0.97 0.2 0.2 0.2 0.1 0.3 0.93 0.51 0.84 0.3 0.3 0.1 0.1 0.2 0.89 0.43 0.85 0.3 0.4 0.1 0.1 0.1 0.84 0.39 0.96 0.4 0.3 0.1 0.1 0.1 0.87 0.40 0.91
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