How can we perform an efficient 3D reconstruction with high accuracy and completeness, in the presence of non-Lambertian surface and low textured regions? This paper aims at fast quality 3D reconstruction, best near real time. While deep learning approaches perform very well in multi-view stereo (MVS), the high complexity of models makes them inapplicable in practical applications. Few works were explored to accelerate deep learning-based 3D reconstruction approaches. In this paper, we take an unprecedented attempt to compress and accelerate these models via pruning their redundant parameters. We introduce an efficient channel pruning method for 2D convolutional neural networks (CNNs) based on a mixed back propagation process, where a soft mask is learned to prune the channels using a fast iterative shrinkage-thresholding algorithm. While in 3D CNNs, we train a large multi-scale CNNs architecture and observe that only utilizing one module enough for the 3D reconstruction, which can still maintain the performance of the full-precision model. We achieve an efficient MVS reconstruction system up to 2 times faster, in contrast to the state-of-the-arts, while maintaining comparable model accuracy and even better completeness.

在非朗伯表面和低纹理区域的情况下，我们如何执行高效，高精度和完整性的3D重建？本文旨在实现最快实时的最佳3D重建质量。虽然深度学习方法在多视图立体声（MVS）中表现很好，但是模型的高度复杂性使其无法在实际应用中使用。很少有人探索可加速基于深度学习的3D重建方法的工作。在本文中，我们进行了史无前例的尝试，通过修剪它们的冗余参数来压缩和加速这些模型。我们介绍了一种基于混合反向传播过程的2D卷积神经网络（CNN）的有效信道修剪方法，其中学习了软掩膜以使用快速迭代收缩阈值算法修剪通道。在3D CNN中，我们训练了一个大型的多尺度CNN体系结构，并观察到仅利用一个足以进行3D重建的模块，仍然可以保持全精度模型的性能。与最先进的技术相比，我们实现了高效的MVS重建系统，速度提高了2倍，同时保持了可比的模型精度甚至更好的完整性。