# Flow-Motion and Depth Network for Monocular Stereo and Beyond

arXiv: https://arxiv.org/abs/1909.05452 · HTML: https://mlsanity.com/paper/1909.05452/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 30 of 37)

- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [π3: Permutation-Equivariant Visual Geometry Learning](https://mlsanity.com/paper/2507.13347/) (2025)
- [MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision](https://mlsanity.com/paper/2410.19115/) (2024)
- [MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details](https://mlsanity.com/paper/2507.02546/) (2025)
- [GIM: Learning Generalizable Image Matcher From Internet Videos](https://mlsanity.com/paper/2402.11095/) (2024)
- [Dens3R: A Foundation Model for 3D Geometry Prediction](https://mlsanity.com/paper/2507.16290/) (2025)
- [Deep Two-View Structure-from-Motion Revisited](https://mlsanity.com/paper/2104.00556/) (2021)
- [GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors](https://mlsanity.com/paper/2504.01016/) (2025)
- [LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction](https://mlsanity.com/paper/2604.14141/) (2026)
- [WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool](https://mlsanity.com/paper/2509.05296/) (2025)
- [Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey](https://mlsanity.com/paper/2507.14501/) (2025)
- [ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training](https://mlsanity.com/paper/2603.04385/) (2026)
- [AMB3R: Accurate Feed-forward Metric-scale 3D Reconstruction with Backend](https://mlsanity.com/paper/2511.20343/) (2025)
- [Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels](https://mlsanity.com/paper/2603.02573/) (2026)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation](https://mlsanity.com/paper/2305.01732/) (2023)
- [MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources](https://mlsanity.com/paper/2601.22054/) (2026)
- [MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement](https://mlsanity.com/paper/2607.17967/) (2026)
- [D'ej`a View: Looping Transformers for Multi-View 3D Reconstruction](https://mlsanity.com/paper/2605.30215/) (2026)
- [Semi-supervised Deep Multi-view Stereo](https://mlsanity.com/paper/2207.11699/) (2022)
- [RobustMVS: Single Domain Generalized Deep Multi-view Stereo](https://mlsanity.com/paper/2405.09131/) (2024)
- [TrajVG: 3D Trajectory-Coupled Visual Geometry Learning](https://mlsanity.com/paper/2602.04439/) (2026)
- [Uncertainty-Driven Dense Two-View Structure from Motion](https://mlsanity.com/paper/2302.00523/) (2023)
- [MotionCrafter: Dense Geometry and Motion Reconstruction with a 4D VAE](https://mlsanity.com/paper/2602.08961/) (2026)
- [DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation](https://mlsanity.com/paper/2603.03744/) (2026)
- [Towards Consistent Video Geometry Estimation](https://mlsanity.com/paper/2605.30060/) (2026)
- [VLM3: Vision Language Models Are Native 3D Learners](https://mlsanity.com/paper/2605.30561/) (2026)
- [SurGe: Improved Surface Geometry in Point Maps](https://mlsanity.com/paper/2605.31577/) (2026)
- [PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation](https://mlsanity.com/paper/2608.16984/) (2026)
- [UniT: Unified Geometry Learning with Group Autoregressive Transformer](https://mlsanity.com/paper/2605.21131/) (2026)

## Abstract (arXiv)

We propose a learning-based method that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera with known intrinsic calibration, our network estimates relative camera poses and the depth map of the source image. The core contribution of the proposed method is threefold. First, a network is tailored for static scenes that jointly estimates the optical flow and camera motion. By the joint estimation, the optical flow search space is gradually reduced resulting in an efficient and accurate flow estimation. Second, a novel triangulation layer is proposed to encode the estimated optical flow and camera motion while avoiding common numerical issues caused by epipolar. Third, beyond two-view depth estimation, we further extend the above networks to fuse depth information from multiple target images and estimate the depth map of the source image. To further benefit the research community, we introduce tools to generate photorealistic structure-from-motion datasets such that deep networks can be well trained and evaluated. The proposed method is compared with previous methods and achieves state-of-the-art results within less time. Images from real-world applications and Google Earth are used to demonstrate the generalization ability of the method.