{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T14:58:28Z","timestamp":1772549908472,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Hunan Province, China","award":["2022JJ30746"],"award-info":[{"award-number":["2022JJ30746"]}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["61976224"],"award-info":[{"award-number":["61976224"]}]},{"name":"National Natural Science Foundation of China","award":["2022JJ30746"],"award-info":[{"award-number":["2022JJ30746"]}]},{"name":"National Natural Science Foundation of China","award":["61976224"],"award-info":[{"award-number":["61976224"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The depth completion task aims to generate a dense depth map from a sparse depth map and the corresponding RGB image. As a data preprocessing task, obtaining denser depth maps without affecting the real-time performance of downstream tasks is the challenge. In this paper, we propose a lightweight depth completion network based on secondary guidance and spatial fusion named SGSNet. We design the image feature extraction module to better extract features from different scales between and within layers in parallel and to generate guidance features. Then, SGSNet uses the secondary guidance to complete the depth completion. The first guidance uses the lightweight guidance module to quickly guide LiDAR feature extraction with the texture features of RGB images. The second guidance uses the depth information completion module for sparse depth map feature completion and inputs it into the DA-CSPN++ module to complete the dense depth map re-guidance. By using a lightweight bootstrap module, the overall network runs ten times faster than the baseline. The overall network is relatively lightweight, up to thirty frames, which is sufficient to meet the speed needs of large SLAM and three-dimensional reconstruction for sensor data extraction. At the time of submission, the accuracy of the algorithm in SGSNet ranked first in the KITTI ranking of lightweight depth completion methods. It was 37.5% faster than the top published algorithms in the rank and was second in the full ranking.<\/jats:p>","DOI":"10.3390\/s22176414","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"6414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SGSNet: A Lightweight Depth Completion Network Based on Secondary Guidance and Spatial Fusion"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9547-8530","authenticated-orcid":false,"given":"Baifan","family":"Chen","sequence":"first","affiliation":[{"name":"The School of Automation, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotian","family":"Lv","sequence":"additional","affiliation":[{"name":"The School of Automation, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongliang","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Automation Equipment, Beijing 100074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Jiao","sequence":"additional","affiliation":[{"name":"Beijing Institute of Automation Equipment, Beijing 100074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dey, A., Jarvis, G., Sandor, C., and Reitmayr, G. (2012, January 5\u20138). Tablet versus phone: Depth perception in handheld augmented reality. Proceedings of the 2012 IEEE international symposium on mixed and augmented reality (ISMAR), Altanta, GA, USA.","DOI":"10.1109\/ISMAR.2012.6402556"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Song, X., Dai, Y., Zhou, D., Liu, L., Li, W., Li, H., and Yang, R. (2020, January 13\u201319). Channel attention based iterative residual learning for depth map super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00567"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1089\/cpb.2007.9935","article-title":"Depth perception in virtual reality: Distance estimations in peri-and extrapersonal space","volume":"11","author":"Wolter","year":"2008","journal-title":"Cyberpsychol. Behav."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.trit.2016.03.009","article-title":"A framework for multi-session RGBD SLAM in low dynamic workspace environment","volume":"1","author":"Wang","year":"2016","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Park, J., Joo, K., Hu, Z., Liu, C.-K., and Kweon, I.S. (2020, January 23\u201328). Non-local spatial propagation network for depth completion. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58601-0_8"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cui, Z., Xu, C., Yan, Y., Sebe, N., and Yang, J. (2019, January 15\u201320). Pattern-affinitive propagation across depth, surface normal and semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00423"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ma, F., Cavalheiro, G.V., and Karaman, S. (2019, January 20\u201324). Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793637"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ma, F., and Karaman, S. (2018, January 21\u201325). Sparse-to-dense: Depth prediction from sparse depth samples and a single image. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460184"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hu, M., Wang, S., Li, B., Ning, S., Fan, L., and Gong, X. (June, January 30). Penet: Towards precise and efficient image guided depth completion. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561035"},{"key":"ref_10","unstructured":"Chen, Y., Yang, B., Liang, M., and Urtasun, R. (November, January 27). Learning joint 2d-3d representations for depth completion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1808","DOI":"10.1109\/LRA.2021.3060396","article-title":"DenseLiDAR: A real-time pseudo dense depth guided depth completion network","volume":"6","author":"Gu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Qiu, J., Cui, Z., Zhang, Y., Zhang, X., Liu, S., Zeng, B., and Pollefeys, M. (2019). Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image. arXiv.","DOI":"10.1109\/CVPR.2019.00343"},{"key":"ref_13","first-page":"2136","article-title":"FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion","volume":"35","author":"Liu","year":"2021","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5264","DOI":"10.1109\/TIP.2021.3079821","article-title":"Adaptive context-aware multi-modal network for depth completion","volume":"30","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., and Geiger, A. (2017, January 10\u201312). Sparsity invariant cnns. Proceedings of the 2017 International Conference on 3D Vision (3DV), Qingdao, China.","DOI":"10.1109\/3DV.2017.00012"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hua, J., and Gong, X. (2018, January 13\u201319). A normalized convolutional neural network for guided sparse depth upsampling. Proceedings of the IJCAI, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/316"},{"key":"ref_17","unstructured":"Eldesokey, A., Felsberg, M., and Khan, F.S. (2018). Propagating confidences through cnns for sparse data regression. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1109\/TIP.2019.2960589","article-title":"Hms-net: Hierarchical multi-scale sparsity-invariant network for sparse depth completion","volume":"29","author":"Huang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Eldesokey, A., Felsberg, M., Holmquist, K., and Persson, M. (2020, January 14\u201319). Uncertainty-aware cnns for depth completion: Uncertainty from beginning to end. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01203"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Van Gansbeke, W., Neven, D., De Brabandere, B., and Van Gool, L. (2019, January 27\u201331). Sparse and noisy lidar completion with rgb guidance and uncertainty. Proceedings of the 2019 16th international conference on machine vision applications (MVA), Tokyo, Japan.","DOI":"10.23919\/MVA.2019.8757939"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TIP.2020.3040528","article-title":"Learning guided convolutional network for depth completion","volume":"30","author":"Tang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yan, Z., Wang, K., Li, X., Zhang, Z., Xu, B., Li, J., and Yang, J. (2021). RigNet: Repetitive image guided network for depth completion. arXiv.","DOI":"10.1007\/978-3-031-19812-0_13"},{"key":"ref_23","first-page":"2366","article-title":"Depth map prediction from a single image using a multi-scale deep network","volume":"27","author":"Eigen","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, A., Yuan, Z., Ling, Y., Chi, W., and Zhang, C. (2020, January 4\u20138). A multi-scale guided cascade hourglass network for depth completion. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV45572.2020.9093407"},{"key":"ref_25","unstructured":"Xu, Y., Zhu, X., Shi, J., Zhang, G., Bao, H., and Li, H. (November, January 27). Depth completion from sparse lidar data with depth-normal constraints. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jaritz, M., De Charette, R., Wirbel, E., Perrotton, X., and Nashashibi, F. (2018, January 5\u20138). Sparse and dense data with cnns: Depth completion and semantic segmentation. Proceedings of the 2018 International Conference on 3D Vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00017"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., and Liu, Z. (2020, January 13\u201319). Dynamic convolution: Attention over convolution kernels. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref_28","first-page":"1519","article-title":"Learning affinity via spatial propagation networks","volume":"30","author":"Liu","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wang, P., and Yang, R. (2018, January 8\u201314). Depth estimation via affinity learned with convolutional spatial propagation network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01270-0_7"},{"key":"ref_30","first-page":"10615","article-title":"Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion","volume":"34","author":"Cheng","year":"2020","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qiao, S., Zhu, Y., Adam, H., Yuille, A., and Chen, L.C. (2021, January 20\u201325). Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00399"},{"key":"ref_34","first-page":"1","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, Z., Yin, H., and Yao, J. (2020, January 25\u201328). Deformable spatial propagation networks for depth completion. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9191138"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:15:18Z","timestamp":1760141718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,25]]},"references-count":36,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176414"],"URL":"https:\/\/doi.org\/10.3390\/s22176414","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,25]]}}}