# Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry

arXiv: https://arxiv.org/abs/2210.01723 · HTML: https://mlsanity.com/paper/2210.01723/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [DF-VO: What Should Be Learnt for Visual Odometry?](https://mlsanity.com/paper/2103.00933/) (2021)
- [Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry](https://mlsanity.com/paper/1903.00912/) (2019)
- [Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry](https://mlsanity.com/paper/2101.05995/) (2021)

## Built on by (most cited first, 6 of 6)

- [Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach](https://mlsanity.com/paper/2305.06121/) (2023)
- [Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain](https://mlsanity.com/paper/2406.16143/) (2024)
- [Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning](https://mlsanity.com/paper/2401.10857/) (2024)
- [Semi-Supervised Pipe Video Temporal Defect Interval Localization](https://mlsanity.com/paper/2407.15170/) (2024)
- [Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes](https://mlsanity.com/paper/2503.04235/) (2025)
- [MDE-VIO: Enhancing Visual-Inertial Odometry Using Learned Depth Priors](https://mlsanity.com/paper/2602.11323/) (2026)

## Abstract (arXiv)

Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality. However, monocular systems suffer from the scale ambiguity problem due to the lack of depth information in 2D frames. This paper contributes by showing an application of the dense prediction transformer model for scale estimation in monocular visual odometry systems. Experimental results show that the scale drift problem of monocular systems can be reduced through the accurate estimation of the depth map by this model, achieving competitive state-of-the-art performance on a visual odometry benchmark.