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

arXiv: https://arxiv.org/abs/1705.00534 · HTML: https://mlsanity.com/paper/1705.00534/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis](https://mlsanity.com/paper/2112.12577/) (2021)
- [Vision-Based Environmental Perception for Autonomous Driving](https://mlsanity.com/paper/2212.11453/) (2022)
- [Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks](https://mlsanity.com/paper/2211.06814/) (2022)
- [Predicting Relative Depth between Objects from Semantic Features](https://mlsanity.com/paper/2101.04626/) (2021)
- [MSFNet:Multi-scale features network for monocular depth estimation](https://mlsanity.com/paper/2107.06445/) (2021)

## Abstract (arXiv)

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.