mlsanityData to 14 Oct 2026

Improving Depth Estimation using Location Information

arXiv 2112.13925 ↗First posted 27 Dec 2021cs.CV cs.AI cs.CYcited by 0

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Abstract (arXiv)

The ability to accurately estimate depth information is crucial for many autonomous applications to recognize the surrounded environment and predict the depth of important objects. One of the most recently used techniques is monocular depth estimation where the depth map is inferred from a single image. This paper improves the self-supervised deep learning techniques to perform accurate generalized monocular depth estimation. The main idea is to train the deep model to take into account a sequence of the different frames, each frame is geotagged with its location information. This makes the model able to enhance depth estimation given area semantics. We demonstrate the effectiveness of our model to improve depth estimation results. The model is trained in a realistic environment and the results show improvements in the depth map after adding the location data to the model training phase.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 40 of its 58 references that are in the map. First posted on arXiv 27 Dec 2021.

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40 of 58 references.

References · 40

  1. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
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  3. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
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  6. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  7. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  8. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  9. 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
  10. 2016DeMoN: Depth and Motion Network for Learning Monocular Stereocited by 201
  11. 2016Single-Image Depth Perception in the Wildcited by 180
  12. 2018Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraintscited by 179
28 more
  1. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  2. 2018Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videoscited by 155
  3. 2017CNN-SLAM: Real-time dense monocular SLAM with learned depth predictioncited by 152
  4. 2015Stereo Matching by Training a Convolutional Neural Network to Compare Image Patchescited by 151
  5. 2018DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistencycited by 148
  6. 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
  7. 2017Occlusion Aware Unsupervised Learning of Optical Flowcited by 101
  8. 2018Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometrycited by 85
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  13. 2019Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depthcited by 50
  14. 2018LEGO: Learning Edge with Geometry all at Once by Watching Videoscited by 47
  15. 2018Unsupervised Adversarial Depth Estimation using Cycled Generative Networkscited by 45
  16. 2018What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?cited by 44
  17. 2017Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistencycited by 40
  18. 2018Single View Stereo Matchingcited by 39
  19. 2018Learning monocular depth estimation with unsupervised trinocular assumptionscited by 36
  20. 2018Towards real-time unsupervised monocular depth estimation on CPUcited by 26
  21. 2019Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learningcited by 13
  22. 2018A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimationcited by 11
  23. 2018Size-to-depth: A New Perspective for Single Image Depth Estimationcited by 5
  24. 2021DeepLoc: A Ubiquitous Accurate and Low-Overhead Outdoor Cellular Localization Systemcited by 5
  25. 2020DynamicSLAM: Leveraging Human Anchors for Ubiquitous Low-Overhead Indoor Localizationcited by 4
  26. 2019GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mappingcited by 3
  27. 2021The Effect of Ground Truth Accuracy on the Evaluation of Localization Systemscited by 3
  28. 2021The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phonescited by 3

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