# BaseBoostDepth: Exploiting Larger Baselines For Self-supervised Monocular Depth Estimation

arXiv: https://arxiv.org/abs/2407.20437 · HTML: https://mlsanity.com/paper/2407.20437/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 37 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation](https://mlsanity.com/paper/1805.09806/) (2018)
- [The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth](https://mlsanity.com/paper/2104.14540/) (2021)
- [Evaluation of CNN-based Single-Image Depth Estimation Methods](https://mlsanity.com/paper/1805.01328/) (2018)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2211.13202/) (2022)
- [Self-Supervised Monocular Depth Estimation with Internal Feature Fusion](https://mlsanity.com/paper/2110.09482/) (2021)
- [Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2112.13047/) (2021)
- [MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera](https://mlsanity.com/paper/2011.11814/) (2020)
- [Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency](https://mlsanity.com/paper/2102.02629/) (2021)
- [Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics](https://mlsanity.com/paper/1906.05717/) (2019)
- [Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth](https://mlsanity.com/paper/2203.15174/) (2022)
- [Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction](https://mlsanity.com/paper/2004.10681/) (2020)
- [Self-supervised Monocular Depth Estimation: Let's Talk About The Weather](https://mlsanity.com/paper/2307.08357/) (2023)
- [Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving](https://mlsanity.com/paper/2403.17301/) (2024)
- [When the Sun Goes Down: Repairing Photometric Losses for All-Day Depth Estimation](https://mlsanity.com/paper/2206.13850/) (2022)
- [From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction](https://mlsanity.com/paper/2203.08122/) (2022)
- [Digging into contrastive learning for robust depth estimation with diffusion models](https://mlsanity.com/paper/2404.09831/) (2024)
- [Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter](https://mlsanity.com/paper/2208.01489/) (2022)
- [SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments](https://mlsanity.com/paper/2011.04408/) (2020)
- [DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume](https://mlsanity.com/paper/2308.07225/) (2023)
- [Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation](https://mlsanity.com/paper/2403.05056/) (2024)
- [Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth Maps](https://mlsanity.com/paper/2206.03799/) (2022)

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

- [RadarGen: Automotive Radar Point Cloud Generation from Cameras](https://mlsanity.com/paper/2512.17897/) (2025)
- [Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation](https://mlsanity.com/paper/2505.23400/) (2025)

## Abstract (arXiv)

In the domain of multi-baseline stereo, the conventional understanding is that, in general, increasing baseline separation substantially enhances the accuracy of depth estimation. However, prevailing self-supervised depth estimation architectures primarily use minimal frame separation and a constrained stereo baseline. Larger frame separations can be employed; however, we show this to result in diminished depth quality due to various factors, including significant changes in brightness, and increased areas of occlusion. In response to these challenges, our proposed method, BaseBoostDepth, incorporates a curriculum learning-inspired optimization strategy to effectively leverage larger frame separations. However, we show that our curriculum learning-inspired strategy alone does not suffice, as larger baselines still cause pose estimation drifts. Therefore, we introduce incremental pose estimation to enhance the accuracy of pose estimations, resulting in significant improvements across all depth metrics. Additionally, to improve the robustness of the model, we introduce error-induced reconstructions, which optimize reconstructions with added error to the pose estimations. Ultimately, our final depth network achieves state-of-the-art performance on KITTI and SYNS-patches datasets across image-based, edge-based, and point cloud-based metrics without increasing computational complexity at test time. The project website can be found at https://kieran514.github.io/BaseBoostDepth-Project.