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Digging into the multi-scale structure for a more refined depth map and 3D reconstruction

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Abstract

Extracting dense depth from a single image is an important yet challenging computer vision task. Compared with stereo depth estimation, sensing the depth of a scene from monocular images is much more difficult and ambiguous because the epipolar geometry constraints cannot be exploited. The recent development of deep learning technologies has introduced significant progress in monocular depth estimation. This paper aims to explore the effects of multi-scale structures on the performance of monocular depth estimation and further obtain a more refined 3D reconstruction by using our predicted depth and corresponding uncertainty. First, we explore three multi-scale architectures and compare the qualitative and quantitative results of some state-of-the-art approaches. Second, in order to improve the robustness of the system and provide the reliability of the predicted depth for subsequent 3D reconstruction, we estimate the uncertainty of noisy data by modeling such uncertainty in a new loss function. Last, the predicted depth map and corresponding depth uncertainty are incorporated into a monocular reconstruction system. The experiments of monocular depth estimation are mainly performed on the widely used NYU V2 depth dataset, on which the proposed method achieves a state-of-the-art performance. For the 3D reconstruction, the implementation of our proposed framework can reconstruct more smooth and dense models on various scenes.

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References

  1. Li C, Lu B, Zhang Y et al (2018) 3D reconstruction of indoor scenes via image registration. Neural Process Lett 48(3):1281–1304

    Article  Google Scholar 

  2. Dong S, Gao Z, Pirbhulal S et al (2020) IoT-based 3D convolution for video salient object detection. Neural Comput Appl 32(3):735–746

    Article  Google Scholar 

  3. Li J, Zhang Y, Chen Z et al (2019) A novel edge-enabled slam solution using projected depth image information. Neural Comput Appl 2019:1–13

    Google Scholar 

  4. Vidal R, Ma Y, Soatto S, Sastry S (2006) Two-view multibody structure from motion. Int J Comput Vis 68(1):7–25

    Article  Google Scholar 

  5. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

  6. Yu F, Koltun V, Funkhouser T (2017) Dilated residual networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 472–480

  7. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122

  8. Kendall A, Gal Y (2017) What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in neural information processing systems, pp 5574–5584

  9. Tateno K, Tombari F, Laina I, Navab N (2017) Cnn-slam: real-time dense monocular slam with learned depth prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 2

  10. Mur-Artal R, Tardós JD (2017) Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Robot 33(5):1255–1262

    Article  Google Scholar 

  11. Eigen D, Puhrsch C, Fergus R (2014) Depth map prediction from a single image using a multi-scale deep network. In: Advances in neural information processing systems, pp 2366–2374

  12. Eigen D, Fergus R (2015) Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE international conference on computer vision, pp 2650–2658

  13. Liu F, Shen C, Lin G, Reid ID (2016) Learning depth from single monocular images using deep convolutional neural fields. IEEE Trans Pattern Anal Mach Intell 38(10):2024–2039

    Article  Google Scholar 

  14. Li B, Shen C, Dai Y, Van Den Hengel A, He M (2015) Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1119–1127

  15. Wang P, Shen X, Lin Z, Cohen S, Price B, Yuille AL (2015) Towards unified depth and semantic prediction from a single image. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2800–2809

  16. Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 239–248

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  18. Garg R, Vijay Kumar BG, Carneiro G, Reid I (2016) Unsupervised cnn for single view depth estimation: Geometry to the rescue. In: European conference on computer vision. Springer, pp 740–756

  19. Godard C, Mac Aodha O, Brostow GJ (2017) Unsupervised monocular depth estimation with left-right consistency. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 6602–6611

  20. Vijayanarasimhan S, Ricco S, Schmid C, Sukthankar R, Fragkiadaki K (2017) Sfm-net: learning of structure and motion from video. arXiv:1704.07804

  21. Wang C, Miguel Buenaposada J, Zhu R, Lucey S (2018) Learning depth from monocular videos using direct methods. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2022–2030

  22. Poggi M, Tosi F, Mattoccia S (2018) Learning monocular depth estimation with unsupervised trinocular assumptions. In: 2018 international conference on 3D vision (3DV). IEEE, pp 324–333

  23. Zhou T, Brown M, Snavely N, Lowe DG (2017) Unsupervised learning of depth and ego-motion from video. CVPR 2(6):7

    Google Scholar 

  24. Repala VK, Dubey SR (2018) Dual cnn models for unsupervised monocular depth estimation. arXiv:1804.06324

  25. Cao Y, Wu Z, Shen C (2018) Estimating depth from monocular images as classification using deep fully convolutional residual networks. IEEE Trans Circuits Syst Video Technol 28(11):3174–3182

    Article  Google Scholar 

  26. Li B, Dai Y, He M (2018) Monocular depth estimation with hierarchical fusion of dilated cnns and soft-weighted-sum inference. Pattern Recognit 83:328–339

    Article  Google Scholar 

  27. Li R, Xian K, Shen C, Cao Z, Lu H, Hang L (2018) Deep attention-based classification network for robust depth prediction. arXiv:1807.03959

  28. Fu H, Gong M, Wang C, Batmanghelich K, Tao D (2018) Deep ordinal regression network for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2002–2011

  29. Mahjourian R, Wicke M, Angelova A (2018) Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5667–5675

  30. Yin Z, Shi J (2018) Geonet: unsupervised learning of dense depth, optical flow and camera pose. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 2

  31. Zou Y, Luo Z, Huang J-B (2018) Df-net: unsupervised joint learning of depth and flow using cross-task consistency. In: Proceedings of the European conference on computer vision (ECCV), pp 36–53

  32. Jiao J, Cao Y, Song Y, Lau R (2018) Look deeper into depth: monocular depth estimation with semantic booster and attention-driven loss. In: Proceedings of the European conference on computer vision (ECCV), pp 53–69

  33. Zhang Z, Cui Z, Xu C, Jie Z, Li X, Yang J (2018) Joint task-recursive learning for semantic segmentation and depth estimation,. In: European conference on computer vision. Springer, pp 238–255

  34. Babu V, Majumder A, Das K, Kumar S et al (2018) A deeper insight into the undemon: unsupervised deep network for depth and ego-motion estimation. arXiv:1809.00969

  35. Tong T, Li G, Liu X, Gao Q (2017) Image super-resolution using dense skip connections. In: 2017 IEEE international conference on computer vision (ICCV). IEEE, pp 4809–4817

  36. Ji Y, Zhang H, Wu QJ (2018) Salient object detection via multi-scale attention cnn. Neurocomputing 322:130–140

    Article  Google Scholar 

  37. Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder–decoder with atrous separable convolution for semantic image segmentation. In: The European conference on computer vision (ECCV)

  38. Moukari M, Picard S, Simoni L, Jurie F (2018) Deep multi-scale architectures for monocular depth estimation. In: 2018 25th IEEE international conference on image processing (ICIP). IEEE, pp 2940–2944

  39. Blundell C, Cornebise J, Kavukcuoglu K, Wierstra D (2015) Weight uncertainty in neural networks. arXiv:1505.05424

  40. Gal Y, Ghahramani Z (2016) Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, pp 1050–1059

  41. Engel J, Schöps T, Cremers D (2014) Lsd-slam: large-scale direct monocular slam. In: European conference on computer vision. Springer, pp 834–849

  42. Yang N, Wang R, Stückler J, Cremers D (2018) Deep virtual stereo odometry: leveraging deep depth prediction for monocular direct sparse odometry. In: European conference on computer vision. Springer, pp 835–852

  43. Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions. In: ICLR

  44. Zwald L, Lambert-Lacroix S (2012) The berhu penalty and the grouped effect. arXiv:1207.6868

  45. Xu D, Wang W, Tang H, Liu H, Sebe N, Ricci E (2018) Structured attention guided convolutional neural fields for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3917–3925

  46. Xu D, Ricci E, Ouyang W, Wang X, Sebe N (2017) Multi-scale continuous crfs as sequential deep networks for monocular depth estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), vol 1

  47. Nathan Silberman PK, Hoiem D, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: ECCV

  48. Sturm J, Engelhard N, Endres F, Burgard W, Cremers D (2012) A benchmark for the evaluation of rgb-d slam systems. In: 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 573–580

  49. Levin A, Lischinski D, Weiss Y (2004) Colorization using optimization. In: ACM transactions on graphics (tog), vol 23, no 3. ACM, pp 689–694

  50. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis (IJCV) 115(3):211–252

    Article  MathSciNet  Google Scholar 

  51. Pizzoli M, Forster C, Scaramuzza D (2014) Remode: probabilistic, monocular dense reconstruction in real time. In: 2014 IEEE international conference on robotics and automation (ICRA). IEEE, pp 2609–2616

  52. Concha Belenguer A, Civera Sancho J (2015) Dpptam: dense piecewise planar tracking and mapping from a monocular sequence. In: Proceedings of IEEE/RSJ international conference on intelligent robotic systems, no. ART-2015-92153

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Acknowledgements

This work was supported in part by Zhejiang Provincial Science Foundation of China (Grant No. LY18F010004) and Major Scientific Project of Zhejiang Lab (No. 2018DD0ZX01).

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Authors and Affiliations

  1. Institution of Information Science and Electrical Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Yinzhang Ding, Lu Lin, Lianghao Wang, Ming Zhang & Dongxiao Li

  2. Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, Hangzhou, 310027, People’s Republic of China

    Yinzhang Ding, Lu Lin, Lianghao Wang, Ming Zhang & Dongxiao Li

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  1. Yinzhang Ding
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  2. Lu Lin
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  3. Lianghao Wang
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Correspondence to Lianghao Wang.

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Ding, Y., Lin, L., Wang, L. et al. Digging into the multi-scale structure for a more refined depth map and 3D reconstruction. Neural Comput & Applic 32, 11217–11228 (2020). https://doi.org/10.1007/s00521-020-04702-3

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