# Du2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

arXiv: https://arxiv.org/abs/2003.14299 · HTML: https://mlsanity.com/paper/2003.14299/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 23 of 23)

- [Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image](https://mlsanity.com/paper/2110.05655/) (2021)
- [Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning](https://mlsanity.com/paper/2108.05251/) (2021)
- [DC2: Dual-Camera Defocus Control by Learning to Refocus](https://mlsanity.com/paper/2304.03285/) (2023)
- [Facial Depth and Normal Estimation using Single Dual-Pixel Camera](https://mlsanity.com/paper/2111.12928/) (2021)
- [LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network](https://mlsanity.com/paper/2307.09815/) (2023)
- [Learning Depth from Focus in the Wild](https://mlsanity.com/paper/2207.09658/) (2022)
- [A Practical Stereo Depth System for Smart Glasses](https://mlsanity.com/paper/2211.10551/) (2022)
- [Dual-Pixel Raindrop Removal](https://mlsanity.com/paper/2210.13321/) (2022)
- [Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging](https://mlsanity.com/paper/2402.18102/) (2024)
- [Learning Dual-Pixel Alignment for Defocus Deblurring](https://mlsanity.com/paper/2204.12105/) (2022)
- [Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels](https://mlsanity.com/paper/2111.07837/) (2021)
- [Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality](https://mlsanity.com/paper/2301.06143/) (2023)
- [Continuous Cost Aggregation for Dual-Pixel Disparity Extraction](https://mlsanity.com/paper/2306.07921/) (2023)
- [BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring](https://mlsanity.com/paper/2105.14766/) (2021)
- [Sub-millisecond Video Synchronization of Multiple Android Smartphones](https://mlsanity.com/paper/2107.00987/) (2021)
- [Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild](https://mlsanity.com/paper/2211.14017/) (2022)
- [Disparity Estimation Using a Quad-Pixel Sensor](https://mlsanity.com/paper/2409.00665/) (2024)
- [Simulating Dual-Pixel Images From Ray Tracing For Depth Estimation](https://mlsanity.com/paper/2503.11213/) (2025)
- [Incorporating dense metric depth into neural 3D representations for view synthesis and relighting](https://mlsanity.com/paper/2409.03061/) (2024)
- [Revisiting Disparity from Dual-Pixel Images: Physics-Informed Lightweight Depth Estimation](https://mlsanity.com/paper/2411.04714/) (2024)
- [DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning](https://mlsanity.com/paper/2506.14709/) (2025)
- [DP-SfM: Dual-Pixel Structure-from-Motion without Scale Ambiguity](https://mlsanity.com/paper/2605.01852/) (2026)
- [FoundDP: Revisiting Weak Disparity Observability in Dual-Pixel Depth Estimation](https://mlsanity.com/paper/2607.01900/) (2026)

## Abstract (arXiv)

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor, which is increasingly common on consumer cameras. Our network uses a novel architecture to fuse these two sources of information and can overcome the above-mentioned limitations of pure binocular stereo matching. Our method provides a dense depth map with sharp edges, which is crucial for computational photography applications like synthetic shallow-depth-of-field or 3D Photos. Additionally, we avoid the inherent ambiguity due to the aperture problem in stereo cameras by designing the stereo baseline to be orthogonal to the dual-pixel baseline. We present experiments and comparisons with state-of-the-art approaches to show that our method offers a substantial improvement over previous works.