mlsanityData to 14 Oct 2026

LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning

arXiv 2211.08608 ↗First posted 16 Nov 2022cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Advances in neural networks enable tackling complex computer vision tasks such as depth estimation of outdoor scenes at unprecedented accuracy. Promising research has been done on depth estimation. However, current efforts are computationally resource-intensive and do not consider the resource constraints of autonomous devices, such as robots and drones. In this work, we present a fast and battery-efficient approach for depth estimation. Our approach devises model-agnostic curriculum-based learning for depth estimation. Our experiments show that the accuracy of our model performs on par with the state-of-the-art models, while its response time outperforms other models by 71%. All codes are available online at https://github.com/fatemehkarimii/LightDepth.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 47 of its 85 references that are in the map. First posted on arXiv 16 Nov 2022.

Builds on

47 of 85 references.

References · 47

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2021Vision Transformers for Dense Predictioncited by 1,803
  7. 2020Knowledge Distillation: A Surveycited by 1,603
  8. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  9. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  10. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  11. 2020A Survey on Visual Transformercited by 654
  12. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
35 more
  1. 2021Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networkscited by 527
  2. 2018PACT: Parameterized Clipping Activation for Quantized Neural Networkscited by 467
  3. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  4. 2017Sparsity Invariant CNNscited by 326
  5. 2019On The Power of Curriculum Learning in Training Deep Networkscited by 286
  6. 2017Automated Curriculum Learning for Neural Networkscited by 257
  7. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 234
  8. 2015Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fieldscited by 169
  9. 2022BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimationcited by 168
  10. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  11. 2018Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundariescited by 145
  12. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
  13. 2018High Quality Monocular Depth Estimation via Transfer Learningcited by 133
  14. 2022DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimationcited by 118
  15. 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
  16. 2019Depth Completion from Sparse LiDAR Data with Depth-Normal Constraintscited by 108
  17. 2019FastDepth: Fast Monocular Depth Estimation on Embedded Systemscited by 94
  18. 2019Foreground-aware Image Inpaintingcited by 90
  19. 2020Guiding Monocular Depth Estimation Using Depth-Attention Volumecited by 82
  20. 2018Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimationcited by 78
  21. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
  22. 2017Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimationcited by 70
  23. 2014Curriculum Learning of Multiple Taskscited by 66
  24. 2022Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthcited by 58
  25. 2018Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networkscited by 31
  26. 2018Towards real-time unsupervised monocular depth estimation on CPUcited by 26
  27. 2020Real-time single image depth perception in the wild with handheld devicescited by 19
  28. 2022Lightweight Monocular Depth Estimation through Guided Decodingcited by 14
  29. 2018Real-Time Dense Mapping for Self-driving Vehicles using Fisheye Camerascited by 10
  30. 2021Quantization-Guided Training for Compact TinyML Modelscited by 10
  31. 2020Uncertainty depth estimation with gated images for 3D reconstructioncited by 6
  32. 2020Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GPcited by 6
  33. 2020SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devicescited by 5
  34. 2022ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Datacited by 5
  35. 2018Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Mapscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimationcited by 2
  2. 2026DepthART: Scaling Foundation Monocular Depth to Tiny Modelscited by 1