Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Open-Source Projects for 3D Point Clouds

  • Chapter
  • First Online:
Deep Learning for 3D Point Clouds

  • 79 Accesses

Abstract

This chapter delves into the realm of point cloud technologies, emphasizing the significance of open-source projects and frameworks in advancing this field. The central focus is on the OpenPointCloud library, an open-source repository that encompasses a variety of deep learning methods for point cloud compression, processing, and analysis. This library utilizes popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet, offering a robust platform for developers and researchers to engage in innovative point cloud applications. The evolution of point cloud technologies and its increasing relevance across various industries are also highlighted, driven by the growing availability of open-source tools and collaborative platforms that foster innovation and enhance research capabilities. The OpenPointCloud library serves as a pivotal resource, facilitating the development and testing of advanced algorithms and contributing significantly to the open-source community. This initiative not only enriches the diversity and availability of tools but also propels the forward momentum of research in point cloud technologies, underscoring the critical role of open-source projects in the technological landscape.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 159.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://openi.pcl.ac.cn/OpenPointCloud

  2. 2.

    Trustie, funded by the Ministry of Science and Technology, is an open-source platform and community jointly initiated and constructed by a number of well-known universities, scientific research institutions, and software enterprises around the clustering method of software development in the network era. Trustie is committed to systematically researching new software development methods and providing method guidance and practice guide for the construction of open source ecology. Website: https://www.trustie.net.

  3. 3.

    https://openi.pcl.ac.cn/OpenAICoding

  4. 4.

    https://openi.pcl.ac.cn/OpenDatasets

  5. 5.

    https://openi.pcl.ac.cn/OpenHardwareVC

  6. 6.

    https://openi.pcl.ac.cn/OpenPCQA

  7. 7.

    https://openi.pcl.ac.cn/OpenCompression

  8. 8.

    https://openi.pcl.ac.cn/OpenVision

References

  1. W. Gao, G. Li, H. Yuan, R. Hamzaoui, Z. Li, S. Liu, Apccpa’22: 1st international workshop on advances in point cloud compression, processing and analysis, in Proceedings of the 30th ACM International Conference on Multimedia (2022), pp. 7392–7393

    Google Scholar 

  2. T. Qin, G. Li, W. Gao, S. Liu, Multi-grained point cloud geometry compression via dual-model prediction with extended octree, in ACM Transactions on Multimedia Computing, Communications, and Applications (2024)

    Google Scholar 

  3. Y. Shao, W. Gao, S. Liu, G. Li, Advanced patch-based affine motion estimation for dynamic point cloud geometry compression. Sensors 24(10), 3142 (2024)

    Google Scholar 

  4. Y. Shao, F. Song, W. Gao, S. Liu, G. Li, Texture-guided graph transform optimization for point cloud attribute compression. Appl. Sci. 14(10), 4094 (2024)

    Google Scholar 

  5. Y. Shao, X. Yang, W. Gao, S. Liu, G. Li, 3d point cloud attribute compression using diffusion-based texture-aware intra prediction, in IEEE Transactions on Circuits and Systems for Video Technology (2024)

    Google Scholar 

  6. J. Zhang, Y. Chen, G. Liu, W. Gao, G. Li, Efficient point cloud attribute compression framework using attribute-guided graph Fourier transform, in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, 2024), pp. 8426–8430

    Google Scholar 

  7. W. Gao, H. Yuan, G. Li, Z. Li, H. Yuan, Low complexity coding unit decision for video-based point cloud compression. IEEE Trans. Image Proc. 33, 149–162 (2023)

    Article  Google Scholar 

  8. Y. Shao, G. Li, Q. Zhang, W. Gao, S. Liu, Non-rigid registration-based progressive motion compensation for point cloud geometry compression. IEEE Trans. Geosci. Remote Sensing (2023)

    Google Scholar 

  9. F. Song, G. Li, X. Yang, W. Gao, S. Liu, Block-adaptive point cloud attribute coding with region-aware optimized transform. IEEE Trans. Circuits Syst. Video Technol. 33, 4294–4308 (2023)

    Article  Google Scholar 

  10. Y. An, Y. Shao, G. Li, W. Gao, S. Liu, A fast motion estimation method with hamming distance for LiDAR point cloud compression, in 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP) (IEEE, Piscataway, 2022), pp. 1–5

    Google Scholar 

  11. H. Yuan, W. Gao, G. Li, Z. Li, Rate-distortion-guided learning approach with cross-projection information for V-PCC fast CU decision, in Proceedings of the 30th ACM International Conference on Multimedia (2022), pp. 3085–3093

    Google Scholar 

  12. F. Song, G. Li, W. Gao, T.H. Li, Rate-distortion optimized graph for point cloud attribute coding. IEEE Signal Process. Lett. 29, 922–926 (2022)

    Article  Google Scholar 

  13. F. Song, G. Li, X. Yang, W. Gao, T.H. Li, Fine-grained correlation representation for graph-based point cloud attribute compression, in 2022 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2022), pp. 1–6

    Google Scholar 

  14. F. Shen, W. Gao, A rate control algorithm for video-based point cloud compression, in 2021 International Conference on Visual Communications and Image Processing (VCIP) (IEEE, Piscataway, 2021), pp. 1–5

    Google Scholar 

  15. F. Song, Y. Shao, W. Gao, H. Wang, T. Li, Layer-wise geometry aggregation framework for lossless LiDAR point cloud compression. IEEE Trans. Circuits Syst. Video Technol. 31(12), 4603–4616 (2021)

    Article  Google Scholar 

  16. L. Xie, W. Gao, H. Zheng, G. Li, Spcgc: scalable point cloud geometry compression for machine vision, in Proceedings of IEEE International Conference on Robotics and Automation (2024)

    Google Scholar 

  17. L. Xie, W. Gao, H. Zheng, H. Ye, Semantic-aware visual decomposition for point cloud geometry compression, in 2024 Data Compression Conference (DCC) (IEEE, Piscataway, 2024), pp. 595–595

    Google Scholar 

  18. Z. Qi, W. Gao, Variable-rate point cloud geometry compression based on feature adjustment and interpolation, in 2024 Data Compression Conference (DCC) (IEEE, Piscataway, 2024), pp. 63–72

    Google Scholar 

  19. Z. Yu, W. Gao, When dynamic neural network meets point cloud compression: computation-aware variable rate and checkerboard context, in 2024 Data Compression Conference (DCC) (IEEE, Piscataway, 2024), p. 600

    Google Scholar 

  20. L. Xie, W. Gao, S. Fan, Z. Yao, Pdnet: parallel dual-branch network for point cloud geometry compression and analysis, in 2024 Data Compression Conference (DCC) (IEEE, Piscataway, 2024), p. 596

    Google Scholar 

  21. L. Xie, W. Gao, H. Zheng, End-to-end point cloud geometry compression and analysis with sparse tensor, in Proceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis (2022), pp. 27–32

    Google Scholar 

  22. C. Fu, G. Li, R. Song, W. Gao, S. Liu, OctAttention: octree-based large-scale contexts model for point cloud compression, in AAAI Conference on Artificial Intelligence (2022), pp. 625–633

    Google Scholar 

  23. S. Fan, W. Gao, Screen-based 3d subjective experiment software, in Proceedings of the 31st ACM International Conference on Multimedia (2023), pp. 9672–9675

    Google Scholar 

  24. W. Liu, W. Gao, X. Mu, Fast inter-frame motion prediction for compressed dynamic point cloud attribute enhancement, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 4 (2024), pp. 3720–3728

    Google Scholar 

  25. Z. Yang, W. Gao, X. Lu, Danet: density-adaptive network for geometry-based point cloud compression artifacts removal, in 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP) (IEEE, Piscataway, 2023), pp. 1–5

    Google Scholar 

  26. X. Fan, G. Li, D. Li, Y. Ren, W. Gao, T.H. Li, Deep geometry post-processing for decompressed point clouds, in 2022 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2022), pp. 1–6

    Google Scholar 

  27. X. Zhang, G. Liao, W. Gao, G. Li, Tdrnet: Transformer-based dual-branch restoration network for geometry based point cloud compression artifacts, in 2022 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2022), pp. 1–6

    Google Scholar 

  28. Z. Li, G. Li, T.H. Li, S. Liu, W. Gao, Semantic point cloud upsampling. IEEE Trans. Multimedia 25, 3432–3442 (2022)

    Article  Google Scholar 

  29. R. Zhang, W. Gao, G. Li, T.H. Li, Qinet: decision surface learning and adversarial enhancement for quasi-immune completion of diverse corrupted point clouds. IEEE Trans. Geosci. Remote Sensing 60, 1–14 (2022)

    Google Scholar 

  30. R. Bao, Y. Ren, G. Li, W. Gao, S. Liu, Flow-based point cloud completion network with adversarial refinement, in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, 2022), pp. 2559–2563

    Google Scholar 

  31. J. Chen, G. Li, R. Zhang, T.H. Li, W. Gao, Pointivae: invertible variational autoencoder framework for 3d point cloud generation, in 2022 IEEE International Conference on Image Processing (ICIP) (IEEE, Piscataway, 2022), pp. 3216–3220

    Google Scholar 

  32. R. Zhang, J. Chen, W. Gao, G. Li, T.H. Li, Pointot: interpretable geometry-inspired point cloud generative model via optimal transport. IEEE Trans. Circuits Syst. Video Technol. 32(10), 6792–6806 (2022)

    Article  Google Scholar 

  33. S. Fan, W. Gao, G. Li, Salient object detection for point clouds, in European Conference on Computer Vision (2022), pp. 1–19

    Google Scholar 

  34. S. Luo, W. Gao, A general framework for rotation invariant point cloud analysis, in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, 2024), pp. 3665–3669

    Google Scholar 

  35. X. Lu and W. Gao, Attentivenet: detecting small objects for LiDAR point clouds by attending to important points, in 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP) (IEEE, Piscataway, 2023), pp. 1–5

    Google Scholar 

  36. Z. Pan, N. Zhang, W. Gao, S. Liu, G. Li, Less is more: label recommendation for weakly supervised point cloud semantic segmentation, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 5 (2024), pp. 4397–4405

    Google Scholar 

  37. Z. Pan, G. Liu, W. Gao, T. Li, Epcontrast: effective point-level contrastive learning for large-scale point cloud understanding, in 2024 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2024)

    Google Scholar 

  38. N. Zhang, Z. Pan, T.H. Li, W. Gao, G. Li, Improving graph representation for point cloud segmentation via attentive filtering, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2023), pp. 1244–1254

    Google Scholar 

  39. K. Wen, N. Zhang, G. Li, W. Gao, MPVNN: multi-resolution point-voxel non-parametric network for 3d point cloud processing, in 2024 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2024)

    Google Scholar 

  40. X. Mao, H. Yuan, X. Lu, R. Hamzaoui, W. Gao, PCAC-GAN: a sparse-tensor-based generative adversarial network for 3d point cloud attribute compression. Computational Visual Media (2024)

    Google Scholar 

  41. J. Wang, W. Gao, G. Li, Applying collaborative adversarial learning to blind point cloud quality measurement. IEEE Trans. Instrument. Measur. (2023)

    Google Scholar 

  42. D. Yang, W. Gao, G. Li, H. Yuan, J. Hou, S. Kwong, Exploiting manifold feature representation for efficient classification of 3d point clouds. ACM Trans. Multimedia Comput. Commun. Appl. 19(1s), 1–21 (2023)

    Article  Google Scholar 

  43. H. Zheng, W. Gao, Z. Yu, T. Zhao, G. Li, Viewpcgc: view-guided learned point cloud geometry compression, in Proceedings of the 32nd ACM International Conference on Multimedia (2024)

    Google Scholar 

  44. L. Xie, W. Gao, H. Zheng, G. Li, Roi-guided point cloud geometry compression towards human and machine vision, in Proceedings of the 32nd ACM International Conference on Multimedia (2024)

    Google Scholar 

  45. C. Peng, W. Gao, Laplacian matrix learning for point cloud attribute compression with ternary search-based adaptive block partition, in Proceedings of the 32nd ACM International Conference on Multimedia (2024)

    Google Scholar 

  46. S. Luo, B. Qu, W. Gao, Learning robust 3d representation from clip via dual denoising (2024). arXiv preprint arXiv:2407.00905

    Google Scholar 

  47. G. Li, G. Wei, W. Gao, Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024)

    Book  Google Scholar 

  48. G. Li, W. Gao, W. Gao, Introduction, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 1–28

    Google Scholar 

  49. G. Li, W. Gao, W. Gao, Background knowledge, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 29–51

    Google Scholar 

  50. G. Li, W. Gao, W. Gao, Predictive coding, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 53–70

    Book  Google Scholar 

  51. G. Li, W. Gao, W. Gao, Transform coding, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 71–96

    Google Scholar 

  52. G. Li, W. Gao, W. Gao, Quantization techniques, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 97–112

    Book  Google Scholar 

  53. G. Li, W. Gao, W. Gao, Entropy coding, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 113–133

    Book  Google Scholar 

  54. G. Li, W. Gao, W. Gao, MPEG geometry-based point cloud compression (G-PCC) standard, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 135–165

    Google Scholar 

  55. G. Li, W. Gao, W. Gao, AVS point cloud compression standard, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 167–197

    Book  Google Scholar 

  56. G. Li, W. Gao, W. Gao, MPEG video-based point cloud compression (V-PCC) standard, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 199–218

    Book  Google Scholar 

  57. G. Li, W. Gao, W. Gao, MPEG Ai-based 3d graphics coding standard, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 219–241

    Google Scholar 

  58. G. Li, W. Gao, W. Gao, Future work, in Point Cloud Compression: Technologies and Standardization (Springer, Berlin, 2024), pp. 243–250

    Google Scholar 

  59. W. Gao, H. Ye, G. Li, H. Zheng, Y. Wu, L. Xie, OpenPointCloud: an open-source algorithm library of deep learning based point cloud compression, in ACM International Conference on Multimedia (2022), pp. 7347–7350

    Google Scholar 

  60. Y. Zhang, W. Gao, G. Li, Openpointcloud-v2: a deep learning based open-source algorithm library of point cloud processing, in Proceedings of the 1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis (2022), pp. 51–55

    Google Scholar 

  61. W. Gao, H. Yuan, Y. Guo, L. Tao, Z. Cai, G. Li, OpenHardwareVC: an open source library for 8k UHD video coding hardware implementation, in Proceedings of the 30th ACM International Conference on Multimedia (2022), pp. 7339–7342

    Google Scholar 

  62. H. Yuan, W. Gao, Openfastvc: an open source library for video coding fast algorithm implementation, in Proceedings of the 31st ACM International Conference on Multimedia (2023), pp. 9660–9663

    Google Scholar 

  63. J.-X. Zhuang, X. Huang, Y. Yang, J. Chen, Y. Yu, W. Gao, G. Li, J. Chen, T. Zhang, Openmedia: open-source medical image analysis toolbox and benchmark under heterogeneous ai computing platforms, in Chinese Conference on Pattern Recognition and Computer Vision (PRCV) (Springer, Berlin, 2022), pp. 356–367

    Google Scholar 

  64. W. Gao, S. Sun, H. Zheng, Y. Wu, H. Ye, Y. Zhang, Opendmc: an open-source library and performance evaluation for deep-learning-based multi-frame compression, in Proceedings of the 31st ACM International Conference on Multimedia (2023), pp. 9685–9688

    Google Scholar 

  65. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin et al., Tensorflow: large-scale machine learning on heterogeneous distributed systems (2016). arXiv preprint arXiv:1603.04467

    Google Scholar 

  66. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., Pytorch: an imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems, vol. 32 (2019), pp. 8026–8037

    Google Scholar 

  67. T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, Z. Zhang, Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems (2015). arXiv preprint arXiv:1512.01274

    Google Scholar 

  68. R.B. Rusu, S. Cousins, 3d is here: Point cloud library (PCL), in 2011 IEEE International Conference on Robotics and Automation (2011), pp. 1–4

    Google Scholar 

  69. Q.-Y. Zhou, J. Park, V. Koltun, Open3D: a modern library for 3D data processing (2018). arXiv:1801.09847

    Google Scholar 

  70. K. Zampogiannis, C. Fermuller, Y. Aloimonos, Cilantro: a lean, versatile, and efficient library for point cloud data processing, in Proceedings of the 26th ACM International Conference on Multimedia (2018), pp. 1364–1367

    Google Scholar 

  71. H. Butler, B. Chambers, P. Hartzell, C. Glennie, PDAL: an open source library for the processing and analysis of point clouds. Comput. Geosci. 148, 104680 (2021)

    Article  Google Scholar 

  72. M. Krivokuca, P.A. Chou, P. Savill, 8i voxelized surface light field (8iVSLF) dataset. ISO/IEC JTC1/SC29/WG11 MPEG, input document m42914 (2018)

    Google Scholar 

  73. A.X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al., Shapenet: an information-rich 3d model repository (2015). arXiv preprint arXiv:1512.03012

    Google Scholar 

  74. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao, 3D ShapeNets: a deep representation for volumetric shapes, in IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1912–1920

    Google Scholar 

  75. I. Armeni, O. Sener, A.R. Zamir, H. Jiang, I. Brilakis, M. Fischer, S. Savarese, 3D semantic parsing of large-scale indoor spaces, in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 1534–1543

    Google Scholar 

  76. A. Dai, A. X. Chang, M. Savva, M. Halber, T.A. Funkhouser, M. Nießner, ScanNet: richly-annotated 3d reconstructions of indoor scenes, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2432–2443

    Google Scholar 

  77. S. Agarwal, A. Vora, G. Pandey, W. Williams, H. Kourous, J. McBride, Ford multi-AV seasonal dataset. Int. J. Robot. Res. 39(12), 1367–1376 (2020)

    Article  Google Scholar 

  78. A. Geiger, P. Lenz, R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, in IEEE Conference on Computer Vision and Pattern Recognition (2012), pp. 3354–3361

    Google Scholar 

  79. J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, J. Gall, SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences, in IEEE/CVF International Conference on Computer Vision (2019), pp. 9296–9306

    Google Scholar 

  80. C. Lai, J. Han, H. Dong, Tensorlayer 3.0: a deep learning library compatible with multiple backends, in IEEE International Conference on Multimedia and Expo Workshops (2021), pp. 1–3

    Google Scholar 

  81. J. Wang, H. Zhu, H. Liu, Z. Ma, Lossy point cloud geometry compression via end-to-end learning. IEEE Trans. Circuits Syst. Video Technol. 31(12), 4909–4923 (2021)

    Article  Google Scholar 

  82. J. Wang, D. Ding, Z. Li, Z. Ma, Multiscale point cloud geometry compression, in Data Compression Conference (2021), pp. 73–82

    Google Scholar 

  83. D.T. Nguyen, M. Quach, G. Valenzise, P. Duhamel, Learning-based lossless compression of 3d point cloud geometry, in IEEE International Conference on Acoustics, Speech and Signal Processing (2021), pp. 4220–4224

    Google Scholar 

  84. L. Yu, X. Li, C. Fu, D. Cohen-Or, P. Heng, PU-net: point cloud upsampling network, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 2790–2799

    Google Scholar 

  85. Z. Li, G. Li, T.H. Li, S. Liu, W. Gao, Semantic point cloud upsampling. IEEE Trans. Multimedia 25, 3432–3442 (2023)

    Article  Google Scholar 

  86. W. Yan, R. Zhang, J. Wang, S. Liu, T.H. Li, G. Li, Vaccine-style-net: point cloud completion in implicit continuous function space, in Proceedings of the 28th ACM International Conference on Multimedia (2020), pp. 2067–2075

    Google Scholar 

  87. S. Fan, W. Gao, G. Li, Salient object detection for point clouds, in European Conference on Computer Vision (2022), pp. 1–19

    Google Scholar 

  88. C.R. Qi, H. Su, K. Mo, L.J. Guibas, Pointnet: deep learning on point sets for 3d classification and segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 652–660

    Google Scholar 

  89. R. Li, X. Li, C. Fu, D. Cohen-Or, P. Heng, PU-GAN: a point cloud upsampling adversarial network, in Proceedings of the IEEE International Conference on Computer Vision (2019), pp. 7202–7211

    Google Scholar 

  90. G. Qian, A. Abualshour, G. Li, A.K. Thabet, B. Ghanem, PU-GCN: point cloud upsampling using graph convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021), pp. 11683–11692

    Google Scholar 

  91. C.R. Qi, L. Yi, H. Su, L.J. Guibas, Pointnet\(++\): deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inform. Process. Syst. 30, 5099–5108 (2017)

    Google Scholar 

  92. B. Qu, X. Liang, S. Sun, W. Gao, Exploring AIGC video quality: a focus on visual harmony, video-text consistency and domain distribution gap, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2024)

    Google Scholar 

  93. B. Qu, H. Li, W. Gao, Bringing textual prompt to ai-generated image quality assessment, in 2024 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2024)

    Google Scholar 

  94. Y. Wu, L. Xie, S. Sun, W. Gao, Y. Yan, Adaptive intra period size for deep learning-based screen content video coding, in 2024 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (IEEE, Piscataway, 2024)

    Google Scholar 

  95. H. Zheng, W. Gao, End-to-end RGB-D image compression via exploiting channel-modality redundancy, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 7 (2024), pp. 7562–7570

    Google Scholar 

  96. L. Tao, W. Gao, G. Li, C. Zhang, Adanic: towards practical neural image compression via dynamic transform routing, in Proceedings of the IEEE/CVF International Conference on Computer Vision (2023), pp. 16879–16888

    Google Scholar 

  97. Y. Wu, W. Gao, End-to-end lossless compression of high precision depth maps guided by pseudo-residual (2022). arXiv preprint arXiv:2201.03195

    Google Scholar 

  98. Y. Wu, Z. Qi, H. Zheng, L. Tao, W. Gao, Deep image compression with latent optimization and piece-wise quantization approximation, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021), pp. 1926–1930

    Google Scholar 

  99. W. Gao, L. Tao, L. Zhou, D. Yang, X. Zhang, Z. Guo, Low-rate image compression with super-resolution learning, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2020), pp. 154–155

    Google Scholar 

  100. Y. Guo, W. Gao, G. Li, Interpretable task-inspired adaptive filter pruning for neural networks under multiple constraints. Int. J. Comput. Vis. 132, 2060–2076 (2024)

    Article  Google Scholar 

  101. W. Gao, Y. Guo, S. Ma, G. Li, S. Kwong, Efficient neural network compression inspired by compressive sensing. IEEE Trans. Neural Netw. Learn. Syst. 35(2), 1965–1979 (2024)

    Article  Google Scholar 

  102. Y. Guo, W. Gao, Semantic-driven automatic filter pruning for neural networks, in 2022 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2022), pp. 1–6

    Google Scholar 

  103. L. Tao, W. Gao, Efficient channel pruning based on architecture alignment and probability model bypassing, in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, Piscataway, 2021), pp. 3232–3237

    Google Scholar 

  104. Z. Yang, W. Gao, G. Li, Y. Yan, Sur-driven video coding rate control for jointly optimizing perceptual quality and buffer control. IEEE Trans. Image Process. 32, 5451–5464 (2023)

    Article  Google Scholar 

  105. F. Shen, Z. Cai, W. Gao, An efficient rate control algorithm for intra frame coding in avs3, in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, Piscataway, 2021), pp. 3164–3169

    Google Scholar 

  106. H. Yuan, W. Gao, J. Wang, Dynamic computational resource allocation for fast inter frame coding in video conferencing applications, in 2021 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2021), pp. 1–6

    Google Scholar 

  107. W. Gao, Q. Jiang, R. Wang, S. Ma, G. Li, S. Kwong, Consistent quality oriented rate control in HEVC via balancing intra and inter frame coding. IEEE Trans. Ind. Inform. 18(3), 1594–1604 (2021)

    Article  Google Scholar 

  108. H. Yuan, W. Gao, A new coding unit partitioning mode for screen content video coding, in Proceedings of the 2021 5th International Conference on Digital Signal Processing (2021), pp. 66–72

    Google Scholar 

  109. W. Gao, On the performance evaluation of state-of-the-art rate control algorithms for practical video coding and transmission systems, in Proceedings of the 2020 4th International Conference on Video and Image Processing (2020), pp. 179–185

    Google Scholar 

  110. W. Gao, S. Kwong, Q. Jiang, C.-K. Fong, P.H. Wong, W.Y. Yuen, Data-driven rate control for rate-distortion optimization in HEVC based on simplified effective initial QP learning. IEEE Trans. Broadcasting 65(1), 94–108 (2018)

    Article  Google Scholar 

  111. W. Gao, A multi-objective optimization perspective for joint consideration of video coding quality, in 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (IEEE, Piscataway, 2019), pp. 986–991

    Google Scholar 

  112. W. Gao, S. Kwong, Y. Jia, Joint machine learning and game theory for rate control in high efficiency video coding. IEEE Trans. Image Process. 26(12), 6074–6089 (2017)

    Article  MathSciNet  Google Scholar 

  113. W. Gao, S. Kwong, Y. Zhou, H. Yuan, SSIM-based game theory approach for rate-distortion optimized intra frame CTU-level bit allocation. IEEE Trans. Multimedia 18(6), 988–999 (2016)

    Article  Google Scholar 

  114. W. Gao, S. Kwong, H. Yuan, X. Wang, DCT coefficient distribution modeling and quality dependency analysis based frame-level bit allocation for HEVC. IEEE Trans. Circuits Syst. Video Technol. 26(1), 139–153 (2015)

    Article  Google Scholar 

  115. W. Gao, S. Kwong, Phase congruency based edge saliency detection and rate control for perceptual image and video coding, in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, Piscataway, 2016), pp. 000264–000269

    Google Scholar 

  116. H. Yuan, W. Gao, S. Ma, Y. Yan, Divide-and-conquer-based RDO-free CU partitioning for 8k video compression. ACM Trans. Multimedia Comput. Commun. Appl. 20(4), 1–20 (2024)

    Article  Google Scholar 

  117. L. Tao, W. Gao, A hardware implementation of entropy encoder for 8k video coding, in 2022 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2022), pp. 1–6

    Google Scholar 

  118. Y. Guo, W. Gao, S. Ma, G. Li, Accelerating transform algorithm implementation for efficient intra coding of 8k UHD videos. ACM Trans. Multimedia Comput. Commun. Appl. 18(4), 1–20 (2022)

    Article  Google Scholar 

  119. Z. Cai, W. Gao, Efficient fast algorithm and parallel hardware architecture for intra prediction of AVS3, in 2021 IEEE International Symposium on Circuits and Systems (ISCAS) (IEEE, Piscataway, 2021), pp. 1–5

    Google Scholar 

  120. W. Gao, H. Yuan, G. Liao, Z. Guo, J. Chen, Pp8k: a new dataset for 8k UHD video compression and processing. IEEE MultiMedia 30(3), 100–109 (2023)

    Article  Google Scholar 

  121. W. Liu, W. Gao, G. Li, S. Ma, T. Zhao, H. Yuan, Enlarged motion-aware and frequency-aware network for compressed video artifact reduction. IEEE Trans. Circuits Syst. Video Technol. 34(10), 10339–10352 (2024)

    Article  Google Scholar 

  122. X. Zang, W. Gao, G. Li, H. Fang, C. Ban, Z. He, H. Sun, A baseline investigation: transformer-based cross-view baseline for text-based person search, in Proceedings of the 31st ACM International Conference on Multimedia (2023), pp. 7737–7746

    Google Scholar 

  123. G. Liao, W. Gao, G. Li, J. Wang, S. Kwong, Cross-collaborative fusion-encoder network for robust RGB-thermal salient object detection. IEEE Trans. Circuits Syst. Video Technol. 32(11), 7646–7661 (2022)

    Article  Google Scholar 

  124. W. Gao, G. Liao, S. Ma, G. Li, Y. Liang, W. Lin, Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection. IEEE Trans. Circuits Syst. Video Technol. 32(4), 2091–2106 (2021)

    Article  Google Scholar 

  125. Y. Chen, S. Sun, G. Li, W. Gao, T.H. Li, Closing the gap between theory and practice during alternating optimization for GANs. IEEE Trans. Neural Netw. Learn. Syst. 34(10), 14005–14017 (2024)

    Article  MathSciNet  Google Scholar 

  126. Y. Chen, C. Jin, G. Li, T.H. Li, W. Gao, Mitigating label noise in GANs via enhanced spectral normalization. IEEE Trans. Circuits Syst. Video Technol. 33(8), 3924–3934 (2023)

    Article  Google Scholar 

  127. X. Zang, G. Li, W. Gao, Multidirection and multiscale pyramid in transformer for video-based pedestrian retrieval. IEEE Trans. Ind. Inform. 18(12), 8776–8785 (2022)

    Article  Google Scholar 

  128. X. Zang, G. Li, W. Gao, X. Shu, Learning to disentangle scenes for person re-identification. Image Vis. Comput. 116, 104330 (2021)

    Article  Google Scholar 

  129. X. Zang, G. Li, W. Gao, X. Shu, Exploiting robust unsupervised video person re-identification. IET Image Process. 16(3), 729–741 (2022)

    Article  Google Scholar 

  130. Z. Yue, G. Li, W. Gao, Cross-level guided attention for human-object interaction detection, in 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (IEEE, Piscataway, 2023), pp. 284–289

    Google Scholar 

  131. Z. Yao, W. Gao, Iterative saliency aggregation and assignment network for efficient salient object detection in optical remote sensing images. IEEE Trans. Geosci. Remote Sensing (2024)

    Google Scholar 

  132. Y. Sun, Z. Li, S. Wang, W. Gao, Depth-assisted calibration on learning-based factorization for a compressive light field display. Opt. Express 31(4), 5399–5413 (2023)

    Article  Google Scholar 

  133. Y. Sun, Z. Li, L. Li, S. Wang, W. Gao, Optimization of compressive light field display in dual-guided learning, in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, 2022), pp. 2075–2079

    Google Scholar 

  134. W. Gao, S. Fan, G. Li, W. Lin, A thorough benchmark and a new model for light field saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 8003–8019 (2023)

    Google Scholar 

  135. Z. Li, G. Li, T. Li, S. Liu, W. Gao, Information-growth attention network for image super-resolution, in Proceedings of the 29th ACM International Conference on Multimedia (2021), pp. 544–552

    Google Scholar 

  136. L. Zhou, W. Gao, G. Li, H. Yuan, T. Zhao, G. Yue, Disentangled feature distillation for light field super-resolution with degradations, in 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (IEEE, Piscataway, 2023), pp. 116–121

    Google Scholar 

  137. L. Zhou, W. Gao, G. Li, End-to-end spatial-angular light field super-resolution using parallax structure preservation strategy, in 2022 IEEE International Conference on Image Processing (ICIP) (IEEE, Piscataway, 2022), pp. 3396–3400

    Google Scholar 

  138. W. Gao, L. Zhou, L. Tao, A fast view synthesis implementation method for light field applications. ACM Trans. Multimedia Comput. Commun. Appl. 17(4), 1–20 (2021)

    Article  Google Scholar 

  139. X. Zhang, W. Gao, G. Li, Q. Jiang, R. Cong, Image quality assessment–driven reinforcement learning for mixed distorted image restoration. ACM Trans. Multimedia Comput. Commun. Appl. 19(1s), 1–23 (2023)

    Article  Google Scholar 

  140. X. Zhang, W. Gao, H. Yuan, G. Li, JE\({ }^{2}\)NET: joint exploitation and exploration in reinforcement learning based image restoration, in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, 2022), pp. 2090–2094

    Google Scholar 

  141. X. Zhang, W. Gao, HIRL: hybrid image restoration based on hierarchical deep reinforcement learning via two-step analysis, in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, Piscataway, 2022), pp. 2445–2449

    Google Scholar 

  142. Z. Guo, W. Gao, H. Wang, J. Wang, S. Fan, No-reference deep quality assessment of compressed light field images, in 2021 IEEE International Conference on Multimedia and Expo (ICME) (IEEE, Piscataway, 2021), pp. 1–6

    Google Scholar 

  143. G. Liao and W. Gao, Rethinking feature mining for light field salient object detection. ACM Trans. Multimedia Comput. Commun. Appl. (2024)

    Google Scholar 

  144. S. Sun, J. Liu, T.H. Li, H. Li, G. Liu, W. Gao, Streamflow: streamlined multi-frame optical flow estimation for video sequences (2023). arXiv preprint arXiv:2311.17099

    Google Scholar 

  145. R. Liu, J. Huang, W. Gao, T.H. Li, G. Li, Mug-STAN: adapting image-language pretrained models for general video understanding (2023). arXiv preprint arXiv:2311.15075

    Google Scholar 

  146. C. Zhang, W. Gao, Learned rate control for frame-level adaptive neural video compression via dynamic neural network, in European Conference on Computer Vision (Springer, Berlin, 2024)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of ECE, Peking University, Shenzhen, China

    Wei Gao & Ge Li

Authors
  1. Wei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ge Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gao, W., Li, G. (2025). Open-Source Projects for 3D Point Clouds. In: Deep Learning for 3D Point Clouds. Springer, Singapore. https://doi.org/10.1007/978-981-97-9570-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-9570-3_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-9569-7

  • Online ISBN: 978-981-97-9570-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation