论文标题
FC-DCNN：一个密集连接的神经网络，用于立体声估计
FC-DCNN: A densely connected neural network for stereo estimation
论文作者
论文摘要
我们提出了一个新颖的轻型网络，用于立体声估计。我们的网络由一个完全连接的密度连接的神经网络（FC-DCNN）组成，该网络计算整流图像对之间的匹配成本。我们的FC-DCNN方法学习表达功能，并执行一些简单但有效的后处理步骤。密集连接的图层结构将每个层的输出连接到每个后续层的输入。这种网络结构以及我们不使用任何完全连接的层或3D卷积的事实会导致非常轻巧的网络。该网络的输出用于计算匹配成本并创建成本量。我们依靠过滤技术，即中位过滤器和引导滤波器，而不是使用时间和内存的成本接触方法，例如半全球匹配或条件随机字段，而是依靠过滤技术。通过计算左右一致性检查，我们可以摆脱不一致的值。之后，我们在差距上使用分水岭前后的分割，并以删除的不一致。然后，该掩码用于完善最终预测。我们表明，我们的方法分别在米德伯里（Middlebury），基蒂（Kitti）和ETH3D基准测试中评估室内和室外场景都很好地效果很好。我们的完整框架可从https://github.com/thedodo/fc-dcnn获得
We propose a novel lightweight network for stereo estimation. Our network consists of a fully-convolutional densely connected neural network (FC-DCNN) that computes matching costs between rectified image pairs. Our FC-DCNN method learns expressive features and performs some simple but effective post-processing steps. The densely connected layer structure connects the output of each layer to the input of each subsequent layer. This network structure and the fact that we do not use any fully-connected layers or 3D convolutions leads to a very lightweight network. The output of this network is used in order to calculate matching costs and create a cost-volume. Instead of using time and memory-inefficient cost-aggregation methods such as semi-global matching or conditional random fields in order to improve the result, we rely on filtering techniques, namely median filter and guided filter. By computing a left-right consistency check we get rid of inconsistent values. Afterwards we use a watershed foreground-background segmentation on the disparity image with removed inconsistencies. This mask is then used to refine the final prediction. We show that our method works well for both challenging indoor and outdoor scenes by evaluating it on the Middlebury, KITTI and ETH3D benchmarks respectively. Our full framework is available at https://github.com/thedodo/FC-DCNN
