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Learning Approaches to Estimate Depth from RGB Lecture 5 What will - PowerPoint PPT Presentation

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Learning Approaches to Estimate Depth from RGB Lecture 5 What will we learn - Latest Approaches to Depth Estimation based on Machine Learning (DNNs) Why do we need new approaches? Paper1 -> CNNs for Depth estimation Paper2

  1. Learning Approaches to Estimate Depth from RGB Lecture 5

  2. What will we learn - Latest Approaches to Depth Estimation based on Machine Learning (DNNs) Why do we need new approaches? ● Paper1 -> CNNs for Depth estimation ● ● Paper2 -> Semantics for Depth Estimation ● Paper3 -> Differentiable Rendering for Depth Estimation ● Paper4 and Paper5 -> Learned Multi-view geometry

  4. Courtesy figure: Silvio Savarese.

  6. Meta - What is important in DNN research? Priors Architecture + Data Loss

  7. Eigen et al., “Depth Map Prediction from a Single Image using a Multi-Scale Deep Network”, NeurIPS14

  8. Eigen et al.

  9. Tatarchenko et al. “What Do Single-view 3D Reconstruction Networks Learn?”, CVPR19

  10. Kato et al. - “Neural 3D Mesh Renderer”, CVPR18

  11. Rendering 3D model + Parameters

  12. Kato et al. - “Neural 3D Mesh Renderer”, CVPR18

  13. Godard et al. - “Unsupervised Monocular Depth Estimation with Left-Right Consistency”, CVPR17

  14. Zhang et al. - LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery, arxiv

  15. Question - What are Latest Trends in Learning Depth from RGB?

  17. What are Latest Trends in Learning Depth from RGB? 1. Large amount of data + Powerful parametric function approximators (DNNs) 2. Exploit semantics 3. Differentiable Rendering 4. Self supervision from stereo 5. Sparse supervision from Lidar

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