Optics and Precision Engineering, Volume. 26, Issue 5, 1231(2018)
Three-dimentional reconstruction of semantic scene based on RGB-D map
[1] [1] GUPTA S, ARBELEZ P, MALIK J. Perceptual organization and recognition of indoor scenes from RGB-D images [C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013: 564-571.
[2] [2] REN X F, BO L F, FOX D. RGB-(D) scene labeling: features and algorithms [C]. Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2012: 2759-2766.
[3] [3] SILBERMAN N, HOIEM D, KOHLI P, et al.. Indoor segmentation and support inference from RGBD images [C]. Proceedings of the 12th European Conference on Computer Vision, Springer, 2012: 746-760.
[4] [4] LAI K, BO L F, FOX D. Unsupervised feature learning for 3D scene labeling [C]. Proceedings of 2014 IEEE International Conference on Robotics and Automation, IEEE, 2014: 3050-3057.
[5] [5] ROCK J, GUPTA T, THORSEN J, et al.. Completing 3D object shape from one depth image [C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2015: 2484-2493.
[6] [6] MONSZPART A, MELLADO N, BROSTOW G J, et al.. RAPter: rebuilding man-made scenes with regular arrangements of planes [J]. ACM Transactions on Graphics, 2015, 34(4): 103.
[7] [7] FIRMAN M, AODHA O M, JULIER S, et al.. Structured prediction of unobserved voxels from a single depth image [C]. Proceedings of 2016 IEEE Computer Vision and Pattern Recognition, IEEE, 2016: 5431-5440.
[8] [8] GUPTA S, ARBELEZ P, GIRSHICK R, et al.. Aligning 3D models to RGB-D images of cluttered scenes [C]. Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2015: 4731-4740.
[9] [9] SONG S R, XIAO J X. Sliding shapes for 3D object detection in depth images [C]. Proceedings of the 13th European Conference on Computer Vision, Springer, 2014: 634-651.
[10] [10] GEIGER A, WANG CH H. Joint 3D object and layout inference from A single RGB-D image [M]//GALL J, GEHLER P, LEIBE B. Pattern Recognition. Cham: Springer, 2015: 183-195.
[11] [11] NAN L L, XIE K, SHARF A. A search-classify approach for cluttered indoor scene understanding [J]. ACM Transactions on Graphics, 2012, 31(6): 137.
[12] [12] LIN D H, FIDLER S, URTASUN R. Holistic scene understanding for 3D object detection with RGBD cameras [C]. Proceedings of 2013 IEEE International Conference on Computer Vision, IEEE, 2013: 1417-1424.
[13] [13] SONG S, XIAO J. Deep sliding shapes for amodal 3D object detection in RGB-D images [J]. Computer Science, 2015, 139(2): 808-816.
[14] [14] ZHENG B, ZHAO Y B, YU J C, et al.. Beyond point clouds: scene understanding by reasoning geometry and physics [C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013: 3127-3134.
[15] [15] KIM B S, KOHLI P, SAVARESE S. 3D scene understanding by voxel-CRF [C]. Proceedings of 2013 IEEE International Conference on Computer Vision, IEEE, 2013: 1425-1432.
[16] [16] HNE C, ZACH C, COHEN A, et al.. Joint 3D scene reconstruction and class segmentation [C]. Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2013: 97-104.
[17] [17] BLHA M, VOGEL C, RICHARD A, et al.. Large-scale semantic 3D reconstruction: an adaptive multi-resolution model for multi-class volumetric labeling [C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016: 3176-3184.
[18] [18] HANDA A, PATRAUCEAN V, BADRINARAYANAN V, et al.. SceneNet: understanding real world indoor scenes with synthetic data [J]. Computer Science, 2015: 4077-4085.
[19] [19] L CH H, SHEN Y H, LI J H. Depth map inpainting method based on Kinect sensor [J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(5): 1697-1703. (in Chinese)
[21] [21] HU CH SH, ZHAN SH, WU C ZH. Image super-resolution based on deep learning features [J]. Acta Automatica Sinica, 2017, 43(5): 814-821. (in Chinese)
[22] [22] CHANG A X, FUNKHOUSER T, GUIBAS L, et al.. ShapeNet: an information-rich 3D model repository [J]. arXiv: 1512.03012, 2015.
[23] [23] JIA Y Q, SHELHAMER E, DONAHUE J, et al.. Caffe: convolutional architecture for fast feature embedding [C]. Proceedings of the 22nd ACM International Conference on Multimedia, ACM, 2014: 675-678.
[24] [24] NEWCOMBE R A, IZADI S, HILLIGES O, et al.. KinectFusion: real-time dense surface mapping and tracking [C]. Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality, IEEE, 2011: 127-136.
[25] [25] GUO R Q, ZOU CH H, HOIEM D. Predicting complete 3D models of indoor scenes [J]. arXiv: 1504.02437, 2015.
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LIN Jin-hua, WANG Yan-jie. Three-dimentional reconstruction of semantic scene based on RGB-D map[J]. Optics and Precision Engineering, 2018, 26(5): 1231
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Received: Oct. 10, 2017
Accepted: --
Published Online: Aug. 14, 2018
The Author Email: Jin-hua LIN (ljh3832@163.com)