FRCN-Deeper Depth Prediction with Fully Convolutional Residual Networks

本文链接：https://blog.youkuaiyun.com/Gentlemanman/article/details/85090834

文章：

IEEE 3D Vision 2016的文章原文

Contribution

First, we introduce a fully convolutional architecture to depth prediction, endowed with novel up-sampling blocks, that allows for dense output maps of higher resolution and at the same time requires fewer parameters and trains on one order of magnitude fewer data than the state of the art, while outperforming all existing methods on standard benchmark datasets. We further propose a more efficient scheme for upconvolutions and combine it with the concept of residual learning [7] to create up projection blocks for the effective upsampling of feature maps. Last, we train the network by optimizing a loss based on the reverse Huber function (berHu) [40] and demonstrate, both theoretically and experimentally, why it is beneficial and better suited for the task at hand.