mlsanityData to 7 Oct 2026

NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-training

arXiv 2408.14177 ↗First posted 26 Aug 2024cs.CVcited by 2

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Abstract (arXiv)

We introduce NimbleD, an efficient self-supervised monocular depth estimation learning framework that incorporates supervision from pseudo-labels generated by a large vision model. This framework does not require camera intrinsics, enabling large-scale pre-training on publicly available videos. Our straightforward yet effective learning strategy significantly enhances the performance of fast and lightweight models without introducing any overhead, allowing them to achieve performance comparable to state-of-the-art self-supervised monocular depth estimation models. This advancement is particularly beneficial for virtual and augmented reality applications requiring low latency inference. The source code, model weights, and acknowledgments are available at https://github.com/xapaxca/nimbled .

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 26 Aug 2024.

Builds on

33 of 43 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  6. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  7. 2021Vision Transformers for Dense Predictioncited by 1,789
  8. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  9. 2015Spatial Transformer Networkscited by 1,423
  10. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
  11. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  12. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
21 more
  1. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  2. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 646
  3. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  4. 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 375
  5. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  6. 2017Sparsity Invariant CNNscited by 325
  7. 2019Structured Knowledge Distillation for Dense Predictioncited by 277
  8. 2023MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimationcited by 173
  9. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  10. 2023Towards Zero-Shot Scale-Aware Monocular Depth Estimationcited by 100
  11. 2023SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applicationscited by 66
  12. 2023Zero-Shot Metric Depth with a Field-of-View Conditioned Diffusion Modelcited by 29
  13. 2023Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVcited by 21
  14. 2023SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimationcited by 21
  15. 2022Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Mattercited by 18
  16. 2023Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Networkcited by 14
  17. 2023GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenescited by 13
  18. 2023From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth Estimation of Dynamic Objects with Ground Contact Priorcited by 13
  19. 2024The Third Monocular Depth Estimation Challengecited by 10
  20. 2024MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimationcited by 4
  21. 2024TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth Estimationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025The Fourth Monocular Depth Estimation Challengecited by 8
  2. 2026Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimationcited by 0