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Learning Single-Image Depth from Videos using Quality Assessment Networks

arXiv 1806.09573 ↗First posted 25 Jun 2018cs.CVcited by 25

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Abstract (arXiv)

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2018.

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Citing papers per year: 2021: 10; 2022: 8; 2023: 2; 2024: 5.

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  1. 2021Unsupervised Scale-consistent Depth Learning from Videocited by 69
  2. 2024AffordanceLLM: Grounding Affordance from Vision Language Modelscited by 55
  3. 2022Hierarchical Normalization for Robust Monocular Depth Estimationcited by 51
  4. 2022SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenescited by 50
  5. 2021Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Predictioncited by 48
  6. 2023RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptionscited by 47
  7. 2022Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Imagecited by 42
  8. 2021Single Image Depth Estimation: An Overviewcited by 35
  9. 2023Understanding 3D Object Interaction from a Single Imagecited by 33
  10. 2021Structure from Silence: Learning Scene Structure from Ambient Soundcited by 17
  11. 2022Improving Monocular Visual Odometry Using Learned Depthcited by 17
  12. 2021Real-time Monocular Depth Estimation with Sparse Supervision on Mobilecited by 12
13 more
  1. 2022A Survey on RGB-D Datasetscited by 11
  2. 2022Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depthcited by 11
  3. 2022Towards Domain-agnostic Depth Completioncited by 6
  4. 2021DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenescited by 5
  5. 2021Recognizing Scenes from Novel Viewpointscited by 5
  6. 2024Scale-Invariant Monocular Depth Estimation via SSI Depthcited by 4
  7. 2024D4D: An RGBD diffusion model to boost monocular depth estimationcited by 3
  8. 2024Floating No More: Object-Ground Reconstruction from a Single Imagecited by 3
  9. 2021Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI datasetcited by 2
  10. 2021DynOcc: Learning Single-View Depth from Dynamic Occlusion Cuescited by 1
  11. 2024360 in the Wild: Dataset for Depth Prediction and View Synthesiscited by 1
  12. 2021Pseudo Supervised Monocular Depth Estimation with Teacher-Student Networkcited by 0
  13. 2022RLSEP: Learning Label Ranks for Multi-label Classificationcited by 0