More Web Proxy on the site http://driver.im/

research-article

A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View

Authors:

Baocai YinAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 4

Article No.: 110, Pages 1 - 22

https://doi.org/10.1145/3506733

Published: 04 March 2022 Publication History

Abstract

Recovering the geometry of an object from a single depth image is an interesting yet challenging problem. While previous learning based approaches have demonstrated promising performance, they don’t fully explore spatial relationships of objects, which leads to unfaithful and incomplete 3D reconstruction. To address these issues, we propose a Spatial Relationship Preserving Adversarial Network (SRPAN) consisting of 3D Capsule Attention Generative Adversarial Network (3DCAGAN) and 2D Generative Adversarial Network (2DGAN) for coarse-to-fine 3D reconstruction from a single depth view of an object. Firstly, 3DCAGAN predicts the coarse geometry using an encoder-decoder based generator and a discriminator. The generator encodes the input as latent capsules represented as stacked activity vectors with local-to-global relationships (i.e., the contribution of components to the whole shape), and then decodes the capsules by modeling local-to-local relationships (i.e., the relationships among components) in an attention mechanism. Afterwards, 2DGAN refines the local geometry slice-by-slice, by using a generator learning a global structure prior as guidance, and stacked discriminators enforcing local geometric constraints. Experimental results show that SRPAN not only outperforms several state-of-the-art methods by a large margin on both synthetic datasets and real-world datasets, but also reconstructs unseen object categories with a higher accuracy.

References

[1]

Hassan Afzal, Djamila Aouada, Bruno Mirbach, and Björn E. Ottersten. 2018. Full 3D reconstruction of non-rigidly deforming objects. ACM Trans. Multim. Comput. Commun. Appl. 14, 1s (2018), 1–23.

Digital Library

[2]

Jan Bechtold, Maxim Tatarchenko, Volker Fischer, and Thomas Brox. 2021. Fostering generalization in single-view 3D reconstruction by learning a hierarchy of local and global shape priors. In CVPR. 15880–15889.

[3]

Zhiqin Chen and Hao Zhang. 2019. Learning implicit fields for generative shape modeling. In CVPR. 5939–5948.

[4]

Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 2016. 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction. In ECCV. 628–644.

[5]

Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner. 2017. Shape completion using 3D-encoder-predictor CNNs and shape synthesis. In CVPR. 6545–6554.

[6]

Weisheng Dong, Guangming Shi, Xin Li, Kefan Peng, Jinjian Wu, and Zhenhua Guo. 2017. Color-guided depth recovery via joint local structural and nonlocal low-rank regularization. IEEE Trans. Multim. 19, 2 (2017), 293–301.

Digital Library

[7]

Haoqiang Fan, Hao Su, and Leonidas J. Guibas Guibas. 2017. A point set generation network for 3D object reconstruction from a single image. In CVPR. 2463–2471.

[8]

Michael Firman, Oisin Mac Aodha, Simon J. Julier, and Gabriel J. Brostow. 2016. Structured prediction of unobserved voxels from a single depth image. In CVPR. 5431–5440.

[9]

Jingkun Gao, Bin Deng, Yuliang Qin, Xiang Li, and Hongqiang Wang. 2019. Point cloud and 3-D surface reconstruction using cylindrical millimeter-wave holography. IEEE Trans. Instrum. Meas. 68, 12 (2019), 4765–4778.

[10]

Xiang Gao, Shuhan Shen, Lingjie Zhu, Tianxin Shi, Zhiheng Wang, and Zhanyi Hu. 2020. Complete scene reconstruction by merging images and laser scans. IEEE Trans. Circuits Syst. Video Technol. 30, 10 (2020), 3688–3701.

[11]

Rohit Girdhar, David F. Fouhey, Mikel Rodriguez, and Abhinav Gupta. 2016. Learning a predictable and generative vector representation for objects. In ECCV. 484–499.

[12]

Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry. 2018. A papier-Mâché approach to learning 3D surface generation. In CVPR. 216–224.

[13]

Jianwei Guo, Shibiao Xu, Dong-Ming Yan, Zhanglin Cheng, Marc Jaeger, and Xiaopeng Zhang. 2020. Realistic procedural plant modeling from multiple view images. IEEE Trans. Vis. Comput. Graph 26, 2 (2020), 1372–1384.

[14]

Yulan Guo, Mohammed Bennamoun, Ferdous Ahmed Sohel, Min Lu, and Jianwei Wan. 2015. An integrated framework for 3-D modeling, object detection, and pose estimation from point-clouds. IEEE Trans. Instrum. Meas. 64, 3 (2015), 683–693.

[15]

JunYoung Gwak, Christopher B. Choy, Manmohan Chandraker, Animesh Garg, and Silvio Savarese. 2017. Weakly supervised 3D reconstruction with adversarial constraint. In 3DV. 263–272.

[16]

Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, and Yizhou Yu. 2017. High-resolution shape completion using deep neural networks for global structure and local geometry inference. In ICCV. 85–93.

[17]

Zhu Hao, Yebin Liu, Jingtao Fan, Qionghai Dai, and Cao Xun. 2017. Video-based outdoor human reconstruction. IEEE Trans. Circuits Syst. Video Technol. 27, 4 (2017), 760–770.

Digital Library

[18]

Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. 2018. End-to-end recovery of human shape and pose. In CVPR. 7122–7131.

[19]

Angjoo Kanazawa, Shubham Tulsiani, Alexei A. Efros, and Jitendra Malik. 2018. Learning category-specific mesh reconstruction from image collections. In ECCV, Vol. 15. 386–402.

[20]

Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR. 1–15.

[21]

Chen Kong, Chenhsuan Lin, and Simon Lucey. 2017. Using locally corresponding CAD models for dense 3D reconstructions from a single image. In CVPR. 5603–5611.

[22]

Dongping Li, Tianjia Shao, Hongzhi Wu, and Kun Zhou. 2017. Shape completion from a single RGBD image. IEEE Trans. Vis. Comput. Graph 23, 7 (2017), 1809–1822.

Digital Library

[23]

Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. 2019. PU-GAN: A point cloud upsampling adversarial network. In ICCV. 7202–7211.

[24]

Yangyan Li, Angela Dai, Leonidas Guibas, and Matthias Nießner. 2015. Database-assisted object retrieval for real-time 3D reconstruction. Computer Graphics Forum 34, 2 (2015), 435–446.

Digital Library

[25]

Chen-Hsuan Lin, Chen Kong, and Simon Lucey. 2018. Learning efficient point cloud generation for dense 3D object reconstruction. In AAAI. 7114–7121.

[26]

Zhengning Liu, Yanpei Cao, Zhengfei Kuang, Leif Kobbelt, and Shimin Hu. 2021. High-quality textured 3Dshape reconstruction with cascaded fully convolutional networks. IEEE Trans. Vis. Comput. Graph 21, 1 (2021), 83–97.

Digital Library

[27]

Chenlei Lv, Weisi Lin, and Baoquan Zhao:. 2021. Voxel structure-based mesh reconstruction from a 3D point cloud. IEEE Trans. Multim. (2021), 1–15. DOI:.

[28]

Oliver Mattausch, Daniele Panozzo, Claudio Mura, Olga Sorkine-Hornung, and Renato Pajarola. 2014. Object detection and classification from large-scale cluttered indoor scans. Computer Graphics Forum 33, 2 (2014), 11–21.

Digital Library

[29]

Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy networks: Learning 3D reconstruction in function space. In CVPR. 4460–4470.

[30]

Hugues Hoppe and Michael M. Kazhdan. 2013. Screened Poisson surface reconstruction. ACM TOG 32, 3 (2013), 29:1–29:13.

Digital Library

[31]

Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders P. Eriksson, and Eugene Belilovsky. 2020. Few-shot single-view 3-D object reconstruction with compositional priors. In ECCV, Vol. 25. 614–630.

[32]

Aron Monszpart, Nicolas Mellado, Gabriel J. Brostow, and Niloy J. Mitra. 2015. RAPter: Rebuilding man-made scenes with regular arrangements of planes. ACM TOG 34, 4 (2015), 1–12.

Digital Library

[33]

Liangliang Nan, Ke Xie, and Andrei Sharf. 2012. A search-classify approach for cluttered indoor scene understanding. ACM TOG 31, 6 (2012), 137:1-137:10.

Digital Library

[34]

Charlie Nash and Chris K. I. Williams. 2017. The shape variational autoencoder: A deep generative model of part-segmented 3D objects. Computer Graphics Forum 36, 5 (2017), 1–12.

Digital Library

[35]

Richard A. Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J. Davison, Pushmeet Kohli, Jamie Shotton, Steve Hodges, and Andrew W. Fitzgibbon. 2011. KinectFusion: Real-time dense surface mapping and tracking. In ISMAR. 127–136.

[36]

Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning continuous signed distance functions for shape representation. In CVPR. 165–174.

[37]

Pedro O. Pinheiro, Negar Rostamzadeh, and Sungjin Ahn. 2019. Domain-adaptive single-view 3D reconstruction. In ICCV. 7637–7646.

[38]

Jhony K. Pontes, Chen Kong, Sridha Sridharan, Simon Lucey, Anders Eriksson, and Clinton Fookes. 2018. Image2Mesh: A learning framework for single image 3D reconstruction. In ACCV. 365–381.

[39]

Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J. Black. 2018. Generating 3D faces using convolutional mesh autoencoders. In ECCV, Vol. 3. 725–741.

[40]

Danilo Jimenez Rezende, SM Ali Eslami, Shakir Mohamed, Peter Battaglia, Max Jaderberg, and Nicolas Heess. 2016. Unsupervised learning of 3D structure from images. In NIPS. 4996–5004.

[41]

Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, and Andreas Geige. 2017. OctNetFusion: Learning depth fusion from data. In 3DV. 57–66.

[42]

Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. In NIPS. 3856–3866.

[43]

Abhishek Sharma, Oliver Grau, and Mario Fritz. 2016. VCONV-DAE: Deep volumetric shape learning without object labels. In ECCV. 236–250.

[44]

Yifei Shi, Pinxin Long, Kai Xu, Hui Huang, and Yueshan Xiong. 2016. Data-driven contextual modeling for 3D scene understanding. Comput. Graph. 55 (2016), 55–67.

Digital Library

[45]

Ivan Sipiran, Robert Gregor, and Tobias Schreck. 2014. Approximate symmetry detection in partial 3D meshes. Computer Graphics Forum 33, 7 (2014), 131–140.

Digital Library

[46]

Edward Smith and David Meger. 2017. Improved adversarial systems for 3D object generation and reconstruction. In CoRL. 87–96.

[47]

Amir Arsalan Soltani, Haibin Huang, Jiajun Wu, Tejas D. Kulkarni, and Joshua B. Tenenbaum. 2017. Synthesizing 3D shapes via modeling multi-view depth maps and silhouettes with deep generative networks. In CVPR. 2511–2519.

[48]

Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, and Thomas Funkhouser. 2017. Semantic scene completion from a single depth image. In CVPR. 1746–1754.

[49]

Pablo Speciale, Martin R. Oswald, Andrea Cohen, and Marc Pollefeys. 2016. A symmetry prior for convex variational 3D reconstruction. In ECCV, Vol. 8. 313–328.

[50]

Jiapeng Tang, Xiaoguang Han, Junyi Pan, Kui Jia, and Xin Tong. 2019. A skeleton-bridged deep learning approach for generating meshes of complex topologies from single RGB images. In CVPR. 4541–4550.

[51]

Lyne P. Tchapmi, Vineet Kosaraju, Hamid Rezatofighi, Ian D. Reid, and Silvio Savarese. 2019. TopNet: Structural point cloud decoder. In CVPR. 383–392.

[52]

Shubham Tulsiani, Tinghui Zhou, Alexei A. Efros, and Jitendra Malik. 2017. Multi-view supervision for single-view reconstruction via differentiable ray consistency. In CVPR. 209–217.

[53]

Jacob Varley, Chad DeChant, Adam Richardson, Joaquín Ruales, and Peter K. Allen. 2017. Shape completion enabled robotic grasping. In IROS. 2442–2447.

[54]

Lingjing Wang and Yi Fang. 2017. Unsupervised 3D reconstruction from a single image via adversarial learning. arXiv:1711.09312.

[55]

Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. 2018. Pixel2Mesh: Generating 3D mesh models from single RGB images. In ECCV, Vol. 11. 55–71.

[56]

Weiyue Wang, Qiangui Huang, Suya You, Chao Yang, and Ulrich Neumann. 2017. Shape inpainting using 3D generative adversarial network and recurrent convolutional networks. In ICCV. 2298–2306.

[57]

Xiaogang Wang, Marcelo H. Ang, and Gim Hee Lee. 2020. Cascaded refinement network for point cloud completion. In CVPR. 787–796.

[58]

Xin Wen, Tianyang Li, Zhizhong Han, and Yu-Shen Liu. 2020. Point cloud completion by skip-attention network with hierarchical folding. In CVPR. 1936–1945.

[59]

Thomas Whelan, Stefan Leutenegger, Renato F. Salas-Moreno, Ben Glocker, and Andrew J. Davison. 2015. ElasticFusion: Dense SLAM without a pose graph. In Robotics. 1–9.

[60]

Udaranga Wickramasinghe, Edoardo Remelli, Graham Knott, and Pascal Fua. 2020. Voxel2Mesh: 3D mesh model generation from volumetric data. In MICCAI. 299–308.

[61]

Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, and William T. Freeman. 2016. Single image 3D interpreter network. In ECCV. 365–382.

[62]

Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In NIPS. 82–90.

[63]

Jiajun Wu, Chengkai Zhang, Xiuming Zhang, Zhoutong Zhang, William T. Freeman, and Joshua B. Tenenbaum. 2018. Learning shape priors for single-view 3D completion and reconstruction. In ECCV, Vol. 11. 673–691.

[64]

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3D ShapeNets: A deep representation for volumetric shapes. In CVPR. 1912–1920.

[65]

Haozhe Xie, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, and Shengping Zhang. 2019. Pix2Vox: Context-aware 3D reconstruction from single and multi-view images. In ICCV. 2690–2698.

[66]

Haozhe Xie, Hongxun Yao, Shengping Zhang, Shangchen Zhou, and Wenxiu Sun. 2020. Pix2Vox++: Multi-scale context-aware 3D object reconstruction from single and multiple images. Int. J. Comput. Vis. 128, 12 (2020), 2919–2935.

Digital Library

[67]

Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun. 2020. GRNet: Gridding residual network for dense point cloud completion. In ECCV, Vol. 9. 365–381.

[68]

Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomír Mech, and Ulrich Neumann. 2019. DISN: Deep implicit surface network for high-quality single-view 3D reconstruction. In NIPS. 490–500.

[69]

Bo Yang, Stefano Rosa, Andrew Markham, Niki Trigoni, and Hongkai Wen. 2019. 3D object dense reconstruction from a single depth view. IEEE Trans. Pattern Anal. Mach. Intell. 41, 12 (2019), 2820–2834.

[70]

Bo Yang, Sen Wang, Andrew Markham, and Niki Trigoni. 2020. Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction. Int. J. Comput. Vis. (2020), 53–73.

Digital Library

[71]

Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, and Niki Trigoni. 2017. 3D object reconstruction from a single depth view with adversarial learning. ICCV Workshop 112, 518 (2017), 679–688.

[72]

Guandao Yang, Yin Cui, Serge J. Belongie, and Bharath Hariharan. 2018. Learning single-view 3D reconstruction with limited pose supervision. In ECCV, Vol. 15. 90–105.

[73]

Shuo Yang, Min Xu, Haozhe Xie, Stuart W. Perry, and Jiahao Xia. 2021. Single-view 3D object reconstruction from shape priors in memory. In CVPR. 3152–3161.

[74]

Yuan Yao, Nico Schertler, Enrique Rosales, Helge Rhodin, Leonid Sigal, and Alla Sheffer. 2020. Front2Back: Single view 3D shape reconstruction via front to back prediction. In CVPR. 528–537.

[75]

Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. 2018. PCN: Point completion network. In 3DV. 728–737.

[76]

Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, and Augustus Odena. 2019. Self-attention generative adversarial networks. In ICML, Vol. 9. 7354–7363.

[77]

Yang Zhang, Kai Huo, Zhen Liu, Yu Zang, Yongxiang Liu, Xiang Li, Qianyu Zhang, and Cheng Wang. 2020. PGNet: A part-based generative network for 3D object reconstruction. Knowledge Based System 194 (2020), 105574.

[78]

Fang Zhao, Wenhao Wang, Shengcai Liao, and Ling Shao. 2021. Learning anchored unsigned distance functions with gradient direction alignment for single-view garment reconstruction. In ICCV. 12674–12683.

[79]

Meihua Zhao, Gang Xiong, MengChu Zhou, Zhen Shen, and Fei-Yue Wang. 2021. 3D-RVP: A method for 3D object reconstruction from a single depth view using voxel and point. Neurocomputing 430 (2021), 94–103.

[80]

Tianhao Zhao, Songnan Li, King Ngi Ngan, and Fanzi Wu. 2019. 3-D reconstruction of human body shape from a single commodity depth camera. IEEE Trans. Multim. 21, 1 (2019), 114–123.

Digital Library

[81]

Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari. 2019. 3D point capsule networks. In CVPR. 1009–1018.

[82]

Yongheng Zhao, Tolga Birdal, Jan Eric Lenssen, Emanuele Menegatti, Leonidas J. Guibas, and Federico Tombari. 2020. Quaternion equivariant capsule networks for 3D point clouds. In ECCV. 1–19.

[83]

Chuhang Zou, Ersin Yumer, Jimei Yang, Duygu Ceylan, and Derek Hoiem. 2017. 3D-PRNN: Generating shape primitives with recurrent neural networks. In ICCV, Vol. 2. 900–909.

[84]

Nikola Zubic and Pietro Lio. 2021. An effective loss function for generating 3D models from single 2D image without rendering. In Artificial Intelligence Applications and Innovations (AIAI). 309–322.

Cited By

Liu CChen YZhu MHao CLi HWang X(2024)DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth based 3D ReconstructionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3690826Online publication date: 30-Aug-2024
https://doi.org/10.1145/3690826
Peng BSun LLei JLiu BShen HLi WHuang Q(2024)Self-Supervised Monocular Depth Estimation via Binocular Geometric Correlation LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366357020:8(1-19)Online publication date: 13-Jun-2024
https://doi.org/10.1145/3663570
Huang QLi PHuang YShuang FCai Y(2024)Region-Focused Network for Dense CaptioningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364837020:6(1-20)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3648370
Show More Cited By

Index Terms

A Spatial Relationship Preserving Adversarial Network for 3D Reconstruction from a Single Depth View
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction

Recommendations

Dense 3D reconstruction combining depth and RGB information

Dense 3D reconstruction has important applications in many fields. The existing depth information based methods are typically constrained in their effective camera-object distance which should be from 0.4m to 4m. We present a novel method that can ...
Edge-Aware Spatial Propagation Network for Multi-view Depth Estimation
Abstract
Deep learning has made great improvements in multi-view stereo. Recent approaches typically adopt raw images as input and estimate depth through deep networks. However, as a primary geometric cue, edge information, which captures the structures of ...
Self-supervised reflectance-guided 3d shape reconstruction from single-view images
Abstract
3D shape reconstruction from a single-view image is an utterly ill-posed and challenging problem, while multi-view methods can reconstruct an object’s shape only from raw images. However, these raw images should be shot in a static scene, to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 4

November 2022

497 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3514185

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2022

Accepted: 01 December 2021

Revised: 01 November 2021

Received: 01 June 2021

Published in TOMM Volume 18, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China
Beijing Natural Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
602
Total Downloads

Downloads (Last 12 months)91
Downloads (Last 6 weeks)19

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liu CChen YZhu MHao CLi HWang X(2024)DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth based 3D ReconstructionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3690826Online publication date: 30-Aug-2024
https://doi.org/10.1145/3690826
Peng BSun LLei JLiu BShen HLi WHuang Q(2024)Self-Supervised Monocular Depth Estimation via Binocular Geometric Correlation LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366357020:8(1-19)Online publication date: 13-Jun-2024
https://doi.org/10.1145/3663570
Huang QLi PHuang YShuang FCai Y(2024)Region-Focused Network for Dense CaptioningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364837020:6(1-20)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3648370
He WLi ZWang HXu TWang ZHuai BYuan NChen E(2024)Multimodal Dialogue Systems via Capturing Context-aware Dependencies and Ordinal Information of Semantic ElementsACM Transactions on Intelligent Systems and Technology10.1145/364509915:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3645099
Qiu HLi HWu QShi HWang LMeng FXu L(2024)Learning Offset Probability Distribution for Accurate Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363721420:5(1-24)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3637214
Wu XFeng X(2024)Size Invariant Visual Cryptography Schemes With Evolving Threshold Access StructuresIEEE Transactions on Multimedia10.1109/TMM.2023.328257326(1488-1503)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3282573
Lv CZhang DGeng SWu ZHuang H(2023)Color Transfer for Images: A SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363515220:8(1-29)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3635152
Yang CWu XChung M(2023)Enhancement of Information Carrying and Decoding for Visual Cryptography with Error CorrectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361292720:1(1-24)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1145/3612927
Zhang HLi PLiu XYang XAn L(2023)An Iterative Semi-supervised Approach with Pixel-wise Contrastive Loss for Road Extraction in Aerial ImagesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/360637420:3(1-21)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3606374
Liu BLei JPeng BYu CLi WLing N(2023)Novel View Synthesis from a Single Unposed Image via Unsupervised LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/358746719:6(1-23)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1145/3587467
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents