# On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion

arXiv: https://arxiv.org/abs/2106.04007 · HTML: https://mlsanity.com/paper/2106.04007/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [DeMoN: Depth and Motion Network for Learning Monocular Stereo](https://mlsanity.com/paper/1612.02401/) (2016)
- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [DeepV2D: Video to Depth with Differentiable Structure from Motion](https://mlsanity.com/paper/1812.04605/) (2018)
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://mlsanity.com/paper/1802.05522/) (2018)
- [BA-Net: Dense Bundle Adjustment Network](https://mlsanity.com/paper/1806.04807/) (2018)
- [Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction](https://mlsanity.com/paper/1803.03893/) (2018)
- [Feature-metric Loss for Self-supervised Learning of Depth and Egomotion](https://mlsanity.com/paper/2007.10603/) (2020)
- [Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera](https://mlsanity.com/paper/1907.05820/) (2019)
- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning](https://mlsanity.com/paper/1709.06841/) (2017)
- [SfM-Net: Learning of Structure and Motion from Video](https://mlsanity.com/paper/1704.07804/) (2017)
- [DeepSFM: Structure From Motion Via Deep Bundle Adjustment](https://mlsanity.com/paper/1912.09697/) (2019)
- [Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth](https://mlsanity.com/paper/1904.07087/) (2019)
- [Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics](https://mlsanity.com/paper/1906.05717/) (2019)
- [Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications](https://mlsanity.com/paper/2004.05560/) (2020)
- [Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling](https://mlsanity.com/paper/2007.10983/) (2020)
- [Self-Supervised Deep Visual Odometry with Online Adaptation](https://mlsanity.com/paper/2005.06136/) (2020)
- [LS-Net: Learning to Solve Nonlinear Least Squares for Monocular Stereo](https://mlsanity.com/paper/1809.02966/) (2018)
- [Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances](https://mlsanity.com/paper/1910.01765/) (2019)
- [Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation](https://mlsanity.com/paper/2009.03787/) (2020)
- [Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues](https://mlsanity.com/paper/2006.09876/) (2020)

## Built on by (most cited first, 2 of 2)

- [Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2309.05254/) (2023)
- [Towards Accurate Ground Plane Normal Estimation from Ego-Motion](https://mlsanity.com/paper/2212.04224/) (2022)

## Abstract (arXiv)

Structure from motion (SfM) has recently been formulated as a self-supervised learning problem, where neural network models of depth and egomotion are learned jointly through view synthesis. Herein, we address the open problem of how to best couple, or link, the depth and egomotion network components, so that information such as a common scale factor can be shared between the networks. Towards this end, we introduce several notions of coupling, categorize existing approaches, and present a novel tightly-coupled approach that leverages the interdependence of depth and egomotion at training time and at test time. Our approach uses iterative view synthesis to recursively update the egomotion network input, permitting contextual information to be passed between the components. We demonstrate through substantial experiments that our approach promotes consistency between the depth and egomotion predictions at test time, improves generalization, and leads to state-of-the-art accuracy on indoor and outdoor depth and egomotion evaluation benchmarks.