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摘要: 现有基于深度学习的立体匹配算法在学习推理过程中缺乏有效信息交互, 而特征提取和代价聚合两个子模块的特征维度存在差异, 导致注意力方法在立体匹配网络中应用较少、方式单一. 针对上述问题, 本文提出了一种多维注意力特征聚合立体匹配算法. 设计2D注意力残差模块, 通过在原始残差网络中引入无降维自适应2D注意力残差单元, 局部跨通道交互并提取显著信息, 为匹配代价计算提供丰富有效的特征. 构建3D注意力沙漏聚合模块, 以堆叠沙漏结构为骨干设计3D注意力沙漏单元, 捕获多尺度几何上下文信息, 进一步扩展多维注意力机制, 自适应聚合和重新校准来自不同网络深度的代价体. 在三大标准数据集上进行评估, 并与相关算法对比, 实验结果表明所提算法具有更高的预测视差精度, 且在无遮挡的显著对象上效果更佳.Abstract: Existing deep learning-based stereo matching algorithms lack effective information interaction in the learning and reasoning process, and there is difference in feature dimension between feature extraction and cost aggregation, resulting in less and single application of attention methods in stereo matching networks. In order to solve these problems, a multi-dimensional attention feature aggregation stereo matching algorithm was proposed. The two-dimensional (2D) attention residual module is designed by introducing the adaptive 2D attention residual unit without dimensionality reduction into the original residual network. Local cross-channel interaction and extraction of salient information provide abundant and effective features for matching cost calculation. The three-dimensional (3D) attention hourglass aggregation module is constructed by designing a 3D attention hourglass unit with a stacked hourglass structure as the backbone. It captures multi-scale geometric context information and expand the multi-dimensional attention mechanism, adaptively aggregating and recalibrating cost volumes from different network depths. The proposed algorithm is evaluated on three standard datasets and compared with related algorithms. The experimental results show that the proposed algorithm has higher accuracy in predicting disparity and has better effect on unobstructed salient objects.
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图 6 损失函数权重对网络的影响
Fig. 6 The influence of the weight of loss function on network performance
表 1 2D注意力残差单元和联合代价体的参数设置(D表示最大视差, 默认步长为1)
Table 1 Parameter setting of the 2D attention residual unit and combined cost volume (D represents the maximum disparity. The default stride is 1)
层级名称 层级设置 输出维度 ${ { { {{F} }_{\rm{l}}} } / { { {{F} }_{\rm{r}}} } }$ 卷积核尺寸, 通道数, 步长 H×W×3 2D 注意力残差模块 Conv0_1 $3 \times 3,32,$ 步长 = 2 ${1 / 2}H \times {1 / 2}W \times 32$ Conv0_2 $3 \times 3,32,$ ${1 / 2}H \times {1 / 2}W \times 32$ Conv0_3 $3 \times 3,32,$ ${1 / 2}H \times {1 / 2}W \times 32$ Conv1_x $\left[ \begin{aligned} 3 \times 3,32 \\ 3 \times 3,32 \end{aligned} \right] \times 3$ ${1 / 2}H \times {1 / 2}W \times 32$ Conv2_x $\left[ \begin{aligned} 3 \times 3,32 \\ 3 \times 3,32 \end{aligned} \right] \times 16$, 步长 = 2 ${1 / 4}H \times {1 / 4}W \times 64$ Conv3_x $\left[ \begin{aligned} 3 \times 3,32 \\ 3 \times 3,32 \end{aligned} \right] \times 3$ ${1 / 4}H \times {1 / 4}W \times 128$ Conv4_x $\left[ \begin{aligned} 3 \times 3,32 \\ 3 \times 3,32 \end{aligned} \right] \times 3$ ${1 / 4}H \times {1 / 4}W \times 128$ ${ {{F} }_{\rm{l}}}$/${ {{F} }_{\rm{r}}}$ 级联: Conv2_x, Conv3_x, Conv4_x ${1 / 4}H \times {1 / 4}W \times 320$ 联合代价体 ${ {{F} }_{{\rm{gc}}} }$ — ${1 / 4}D \times {1 / 4}H \times {1 / 4}W \times 40$ ${\tilde {{F} }_{\rm{l}}}$/${\tilde {{F} }_{\rm{r}}}$ $\left[ \begin{aligned} 3 \times 3,128 \\ 1 \times 1,{\rm{ } }12 \end{aligned} \right]$ ${1 / 4}H \times {1 / 4}W \times 12$ ${ {{F} }_{{\rm{cat}}} }$ — ${1 / 4}D \times {1 / 4}H \times {1 / 4}W \times 24$ ${ {{F} }_{{\rm{com}}} }$ 级联: ${ {{F} }_{{\rm{gc}}} }$, ${ {{F} }_{{\rm{cat}}} }$ ${1 / 4}D \times {1 / 4}H \times {1 / 4}W \times 64$ 
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表 2 2D注意力残差模块在不同设置下的性能评估
Table 2 Performance evaluation of 2D attention residual module with different settings
网络设置 KITTI2015 2D 注意力单元 > 1 px (%) > 2 px (%) > 3 px (%) EPE (px) — 13.6 3.49 1.79 0.631 最大池化 + 降维 12.9 3.20 1.69 0.623 平均池化 + 降维 12.7 3.26 1.64 0.620 $ \checkmark$ 12.4 3.12 1.61 0.615
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表 3 联合代价体和3D注意力沙漏聚合模块在不同设置下的性能评估
Table 3 Evaluation of 3D attention hourglass aggregation module and combined cost volume with different settings
网络设置 KITTI2012 KITTI2015 联合代价体 3D 注意力单元 EPE (px) D1-all (%) EPE (px) D1-all (%) 3D 最大池化 3D 平均池化 $ \checkmark$ — — 0.804 2.57 0.615 1.94 $ \checkmark$ $ \checkmark$ — 0.722 2.36 0.610 1.70 $ \checkmark$ — $ \checkmark$ 0.703 2.33 0.607 1.68 PSMNet[17] $ \checkmark$ $ \checkmark$ 0.867 2.65 0.652 2.03 $ \checkmark$ $ \checkmark$ $ \checkmark$ 0.654 2.13 0.589 1.43
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表 4 不同算法在SceneFlow数据集上的性能评估
Table 4 Performance evaluation of different methods on the SceneFlow dataset
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表 5 不同算法在KITTI2015上的性能评估 (%)
Table 5 Performance evaluation of different methods on the KITTI2015 dataset (%)
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表 6 不同算法在KITTI2012上的性能评估 (%)
Table 6 Performance evaluation of different methods on the KITTI2012 dataset (%)
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