mlsanityData to 14 Oct 2026

Learning depth from monocular video sequences

arXiv 2310.17156 ↗First posted 26 Oct 2023cs.CVcited by 0

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Abstract (arXiv)

Learning single image depth estimation model from monocular video sequence is a very challenging problem. In this paper, we propose a novel training loss which enables us to include more images for supervision during the training process. We propose a simple yet effective model to account the frame to frame pixel motion. We also design a novel network architecture for single image estimation. When combined, our method produces state of the art results for monocular depth estimation on the KITTI dataset in the self-supervised setting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 26 Oct 2023.

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17 of 29 references.

References · 17

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,241
  5. 2016Pyramid Scene Parsing Networkcited by 2,479
  6. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,161
  7. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  8. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  9. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,070
  10. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  11. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  12. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
5 more
  1. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  2. 2018Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraintscited by 179
  3. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
  4. 2017SfM-Net: Learning of Structure and Motion from Videocited by 89
  5. 2017Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistencycited by 40

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