# StereoDRNet: Dilated Residual Stereo Net

arXiv: https://arxiv.org/abs/1904.02251 · HTML: https://mlsanity.com/paper/1904.02251/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 29 of 29)

- [Attention Concatenation Volume for Accurate and Efficient Stereo Matching](https://mlsanity.com/paper/2203.02146/) (2022)
- [SMD-Nets: Stereo Mixture Density Networks](https://mlsanity.com/paper/2104.03866/) (2021)
- [MoCha-Stereo: Motif Channel Attention Network for Stereo Matching](https://mlsanity.com/paper/2404.06842/) (2024)
- [Toward Practical Monocular Indoor Depth Estimation](https://mlsanity.com/paper/2112.02306/) (2021)
- [Neural Markov Random Field for Stereo Matching](https://mlsanity.com/paper/2403.11193/) (2024)
- [ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks](https://mlsanity.com/paper/2201.02263/) (2022)
- [StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision](https://mlsanity.com/paper/2104.05289/) (2021)
- [Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks](https://mlsanity.com/paper/2112.01011/) (2021)
- [Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2106.00985/) (2021)
- [Mirror3D: Depth Refinement for Mirror Surfaces](https://mlsanity.com/paper/2106.06629/) (2021)
- [Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey](https://mlsanity.com/paper/2405.01725/) (2024)
- [AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach](https://mlsanity.com/paper/2112.04974/) (2021)
- [Vision-Based Environmental Perception for Autonomous Driving](https://mlsanity.com/paper/2212.11453/) (2022)
- [DCVNet: Dilated Cost Volume Networks for Fast Optical Flow](https://mlsanity.com/paper/2103.17271/) (2021)
- [Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction](https://mlsanity.com/paper/2110.02405/) (2021)
- [Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder](https://mlsanity.com/paper/2205.08089/) (2022)
- [EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching](https://mlsanity.com/paper/2207.09796/) (2022)
- [Uncertainty Guided Depth Fusion for Spike Camera](https://mlsanity.com/paper/2208.12653/) (2022)
- [SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching](https://mlsanity.com/paper/2105.11587/) (2021)
- [Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning](https://mlsanity.com/paper/2106.08486/) (2021)
- [Depth Estimation from Single-shot Monocular Endoscope Image Using Image Domain Adaptation And Edge-Aware Depth Estimation](https://mlsanity.com/paper/2201.04485/) (2022)
- [Image-Coupled Volume Propagation for Stereo Matching](https://mlsanity.com/paper/2301.00695/) (2022)
- [InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation](https://mlsanity.com/paper/2309.13516/) (2023)
- [VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors](https://mlsanity.com/paper/2406.02552/) (2024)
- [EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency](https://mlsanity.com/paper/2408.05452/) (2024)
- [Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching](https://mlsanity.com/paper/2110.12769/) (2021)
- [OpenMaterial: A Large-scale Dataset of Complex Materials for 3D Reconstruction](https://mlsanity.com/paper/2406.08894/) (2024)
- [A Lightweight Target-Driven Network of Stereo Matching for Inland Waterways](https://mlsanity.com/paper/2410.07915/) (2024)
- [StereoDiffuer: Diffusion-based Progressive Geometry Modeling with Saliency Attention Perception for Stereo Matching](https://mlsanity.com/paper/2608.21710/) (2026)

## Abstract (arXiv)

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.