mlsanityData to 14 Oct 2026

Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning

arXiv 2401.10857 ↗First posted 19 Jan 2024cs.CV cs.ROcited by 0

Not on the map. Why?

Abstract (arXiv)

Deep learning algorithms have driven expressive progress in many complex tasks. The loss function is a core component of deep learning techniques, guiding the learning process of neural networks. This paper contributes by introducing a consistency loss for visual odometry with deep learning-based approaches. The motion consistency loss explores repeated motions that appear in consecutive overlapped video clips. Experimental results show that our approach increased the performance of a model on the KITTI odometry benchmark.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

11 of 19 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,620
  5. 2021ViViT: A Video Vision Transformercited by 1,620
  6. 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
  7. 2017DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networkscited by 175
  8. 2017UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learningcited by 95
  9. 2019Visual Odometry Revisited: What Should Be Learnt?cited by 57
  10. 2023Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approachcited by 8
  11. 2022Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometrycited by 6

Built on by

Not cited yet.