Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference

2017-04-27 · Bo Li, Yuchao Dai, Huahui Chen, Mingyi He

This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation. Compared with existing deep CNN based methods, our method achieves much better results with fewer training examples and model parameters. The advantages of our method come from the usage of dilated convolution, skip connection architecture and soft-weight-sum inference. Experimental evaluation on the NYU Depth V2 dataset shows that our method outperforms other state-of-the-art methods by a margin.

📄 PDF Abstract BibTeX arXiv:1705.00534

Code (1)

racinmat/depth-voxelmap-estimation tf

Tasks

Depth Estimation

Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

2021-03-12 · Binghan Li, Yindong Hua, Yifeng Liu, Mi Lu

Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently achieved state-of-the-art performance on e…

Depth Estimation Depth Prediction

Learning a Dilated Residual Network for SAR Image Despeckling

2017-09-09 · Qiang Zhang, Qiangqiang Yuan, Jie Li, Zhen Yang 외

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the n…

Sar Image Despeckling

Dilated Residual Networks

2017-05-28 · CVPR 2017 7 · Fisher Yu, Vladlen Koltun, Thomas Funkhouser

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spati…

Classification General Classification image-classification Image Classification+3

Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks

2018-09-03 · Zhixiang Hao, Yu Li, ShaoDi You, Feng Lu

Convolutional Neural Networks have demonstrated superior performance on single image depth estimation in recent years. These works usually use stacked spatial pooling or strided convolution to get high-level information …

Depth Estimation

DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation

2022-12-02 · Haoran Wei, Xu Liu, Shouchun Xu, Zhongjian Dai 외

Many current works directly adopt multi-rate depth-wise dilated convolutions to capture multi-scale contextual information simultaneously from one input feature map, thus improving the feature extraction efficiency for r…

Real-Time Semantic Segmentation Semantic Segmentation