We propose a system that uses a convolution neural network (CNN) to estimate
depth from a stereo pair followed by volumetric fusion of the predicted depth
maps to produce a 3D reconstruction of a scene. Our proposed depth refinement
architecture, predicts view-consistent disparity and occlusion maps that helps
the fusion system to produce geometrically consistent reconstructions. We
utilize 3D dilated convolutions in our proposed cost filtering network that
yields better filtering while almost halving the computational cost in
comparison to state of the art cost filtering architectures.For feature
extraction we use the Vortex Pooling architecture. The proposed method achieves
state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo
benchmarks. Finally, we demonstrate that our system is able to produce high
fidelity 3D scene reconstructions that outperforms the state of the art stereo
system.

提出了一种使用卷积神经网络（CNN）从立体图像估计深度，利用预测的深度图进行体积融合，进而恢复场景的三维重建方法，该方法采用了新的深度细化架构，实现了先进成本滤波架构成本的近半降低，采用风暴池架构进行特征提取，该方法在多个基准数据集上均取得了领先的成果。

StereoDRNet：扩张残差立体网

StereoDRNet: Dilated Residual Stereo Net

Semantic segmentation is a fundamental task in computer vision, which can be
considered as a per-pixel classification problem. Recently, although fully
convolutional neural network (FCN) based approaches have made remarkable
progress in such task, aggregating local and contextual information in
convolutional feature maps is still a challenging problem. In this paper, we
argue that, when predicting the category of a given pixel, the regions close to
the target are more important than those far from it. To tackle this problem,
we then propose an effective yet efficient approach named Vortex Pooling to
effectively utilize contextual information. Empirical studies are also provided
to validate the effectiveness of the proposed method. To be specific, our
approach outperforms the previous state-of-the-art model named DeepLab v3 by
1.5% on the PASCAL VOC 2012 val set and 0.6% on the test set by replacing the
Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed
Vortex Pooling. Moreover, our model (10.13FPS) shares similar computation cost
with DeepLab v3 (10.37 FPS).

通过引入 Vortex Pooling 方法，本文提出了一种有效而高效的语义分割方法，该方法在 PASCAL VOC 2012 中超越了现有的最先进模型 DeepLab v3，并且具有与 DeepLab v3 相似的计算代价。