Abstract
Depth prediction from single image is a challenging task due to the intra scale ambiguity and unavailability of prior information. The prediction of an unambiguous depth from single RGB image is very important aspect for computer vision applications. In this paper, an end-to-end sparse-to-dense network using transformers is proposed for depth estimation. The proposed network processes single images along with the additional sparse depth samples which have been generated for depth estimation. The additional sparse depth sample are acquired either with a low-resolution depth sensor or calculated by visual simultaneous localization. Here, we have proposed a model that utilises both sparse samples and transformers and along with a encoder-decoder structure that helps us in giving great depth results that are comparable to other state-of-the-art results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Chen, J., et al.: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.13645 (2021)
Soleymani, A.A.M.-M., Deep Learning: Transformer Networks (2019)
Ma, F., Karaman, S.: Sparse-to-dense: depth prediction from sparse depth samples and a single image. In: 2018 IEEE International Conference on Robotics and Automation (ICRA) (2018)
Hambarde, P., Murala, S.: S2DNet: depth estimation from single image and sparse samples. IEEE Trans. Comput. Imaging 6, 806–817 (2020)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. arXiv preprint arXiv:2012.15840 (2020)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. (IJRR) 32, 1231–1237 (2013)
Koch, T., Liebel, L., Fraundorfer, F., Körner, M.: Evaluation of CNN-based single-image depth estimation methods. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11131, pp. 331–348. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11015-4_25
Roy, A., Todorovic, S.: Monocular depth estimation using neural regression forest. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Wang, W., Chen, C., Ding, M., Li, J., Yu, H., Zha, S.: TransBTS: multimodal brain tumor segmentation using transformer. arXiv preprint arXiv:2103.04430 (2021)
Han, K., et al.: A survey on visual transformer. arXiv preprint arXiv:2012.12556 (2020)
Karimi, D., Vasylechko, S., Gholipour, A.: Convolution-free medical image segmentation using transformers. arXiv preprint arXiv:2102.13645 (2021)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_54
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Yang, G., Tang, H., Ding, M., Sebe, N., Ricci, E.: Transformers solve the limited receptive field for monocular depth prediction. arXiv preprint arXiv:2103.12091 (2021)
Phutke, S.S., Murala, S.: Diverse receptive field based adversarial concurrent encoder network for image inpainting. IEEE Signal Process. Lett. 28, 1873–1877 (2021)
Mehta, N., Murala, S.: MSAR-Net: multi-scale attention based light-weight image super-resolution. Pattern Recognit. Lett. 151, 215–221 (2021)
Patil, P.W., et al.: An unified recurrent video object segmentation framework for various surveillance environments. IEEE Trans. Image Process. 30, 7889–7902 (2021)
Dudhane, A., Hambarde, P., Patil, P., Murala, S.: Deep underwater image restoration and beyond. IEEE Signal Process. Lett. 27, 675–679 (2020)
Dudhane, A., Biradar, K.M., Patil, P.W., Hambarde, P., Murala, S.: Varicolored image de-hazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4564–4573 (2020)
Hambarde, P., Dudhane, A., Murala, S.: Single image depth estimation using deep adversarial training. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 989–993. IEEE (2019)
Hambarde, P., Dudhane, A., Patil, P.W., Murala, S., Dhall, A.: Depth estimation from single image and semantic prior. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1441–1445. IEEE (2020)
Patil, P.W., Biradar, K.M., Dudhane, A., Murala, S.: An end-to-end edge aggregation network for moving object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8149–8158 (2020)
Patil, P.W., Dudhane, A., Chaudhary, S., Murala, S.: Multi-frame based adversarial learning approach for video surveillance. Pattern Recognit. 122, 108350 (2022)
Hambarde, P., Murala, S., Dhall, A.: UW-GAN: single image DepthEstimation and image enhancement for underwater images. IEEE Trans. Instrum. Meas. 70, 1–12(2021)
Hambarde, P., Talbar, S.N., Sable, N., Mahajan, A., Chavan, S.S., Thakur, M.: Radiomics for peripheral zone and intra-prostatic urethra segmentation in MR imaging. Biomed. Signal Process. Control 51, 19–29 (2019)
Hambarde, P., Talbar, S., Mahajan, A., Chavan, S., Thakur, M., Sable, N.: Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net. Biocybern. Biomed. Eng. 40(4), 1421–1435 (2020)
Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R.: WheatNet-lite: a novel light weight network for wheat head detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1332–1341 (2021)
Alaspure, P., Hambarde, P., Dudhane, A., Murala, S.: DarkGAN: night image enhancement using generative adversarial networks. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds.) CVIP 2020. CCIS, vol. 1376, pp. 293–302. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1086-8_26
Bhagat, S., Kokare, M., Haswani, V., Hambarde, P., Kamble, R.: Eff-UNet++: a novel architecture for plant leaf segmentation and counting. Ecol. Inform. 68, 101583 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Malik, R., Hambarde, P., Murala, S. (2022). Depth Estimation Using Sparse Depth and Transformer. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-11349-9_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11348-2
Online ISBN: 978-3-031-11349-9
eBook Packages: Computer ScienceComputer Science (R0)