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

Deep-Learning-Based Point Cloud Analysis II

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

  • 82 Accesses

Abstract

The emergence of advanced 3D sensing technologies, such as LiDAR, has significantly increased the availability of point cloud data, driving the need for robust analytics through deep learning. Point clouds, with their detailed spatiotemporal structures, are vital across numerous applications, requiring innovative approaches for effective interpretation and utilization. This chapter delves into the intersection of deep learning and point cloud analytics, covering essential tasks like point classification and semantic segmentation. It then explores place recognition, object retrieval, and registration, emphasizing their importance in interpreting dynamic environments. This chapter concludes with an examination of multimodal analysis, showcasing the synergistic potential of integrating point cloud data with other data modalities. Each section systematically unpacks the problems, general solution strategies, seminal contributions, and emerging trends, encapsulating the state-of-the-art in deep-learning-based point cloud analytics and paving the way for future advancements in the field.

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

References

  1. 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 

  2. 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 

  3. 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 

  4. H. Zheng, W. Gao, End-to-end RGB-D image compression via exploiting channel-modality redundancy. Proc. AAAI Conf. Artif. Intell. 38(7), 7562–7570 (2024)

    Google Scholar 

  5. 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. 16 879–16 888

    Google Scholar 

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

    Google Scholar 

  7. 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 

  8. 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 

  9. 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 

  10. Y. Guo, W. Gao, G. Li, Interpretable task-inspired adaptive filter pruning for neural networks under multiple constraints. Int. J. Comput. Vision 132(6) 2060–2076 (2024)

    Article  Google Scholar 

  11. W. Gao, Y. Guo, S. Ma, G. Li, S. Kwong, Efficient neural network compression inspired by compressive sensing. IEEE Trans. Neural Networks Learn. Syst. 35(2), 1965–1979 (2022)

    Article  Google Scholar 

  12. 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 

  13. 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 

  14. 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 Proces. 32, 5451–5464 (2023)

    Article  Google Scholar 

  15. 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 

  16. 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 

  17. 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. Inf. 18(3), 1594–1604 (2021)

    Article  Google Scholar 

  18. 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 

  19. 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 

  20. 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. Broadcast. 65(1), 94–108 (2018)

    Article  Google Scholar 

  21. 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 

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

    Article  MathSciNet  Google Scholar 

  23. 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 

  24. 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 

  25. 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. 000 264–000 269

    Google Scholar 

  26. 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 

  27. 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 

  28. 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 

  29. 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 

  30. 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 

  31. 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 

  32. 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 

  33. 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 

  34. 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 

  35. 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 

  36. 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 Networks Learn. Syst. 35(10), 14005–14017 (2024)

    Article  MathSciNet  Google Scholar 

  37. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. 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 

  42. Z. Yao, W. Gao, Iterative saliency aggregation and assignment network for efficient salient object detection in optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 62, 1–13 (2024)

    Google Scholar 

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

    Article  Google Scholar 

  44. 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 

  45. 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 

  46. 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 

  47. G. Liao, W. Gao, Rethinking feature mining for light field salient object detection. ACM Trans. Multimedia Comput. Commun. Appl. 20(10), 1–24 (2024)

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  50. 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 

  51. 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 

  52. T. Qin, G. Li, W. Gao, S. Liu, Multi-grained point cloud geometry compression via dual-model prediction with extended octree. ACM Trans. Multimedia Comput. Commun. Appl. 20(9), 1–30 (2024)

    Article  Google Scholar 

  53. 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 

  54. 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 

  55. Y. Shao, X. Yang, W. Gao, S. Liu, G. Li, 3d point cloud attribute compression using diffusion-based texture-aware intra prediction. IEEE Trans. Circuits Syst. Video Technol. 34(10), 9633–9646 (2024)

    Article  Google Scholar 

  56. 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 

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

    Article  Google Scholar 

  58. 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 Sens. 61, 1–14 (2023)

    Google Scholar 

  59. 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(8), 4294–4308 (2023)

    Article  Google Scholar 

  60. 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 

  61. 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 

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

    Article  Google Scholar 

  63. 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 

  64. 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 

  65. 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 

  66. 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 

  67. 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 

  68. 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 

  69. 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), pp. 600–600

    Google Scholar 

  70. 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), pp. 596–596

    Google Scholar 

  71. 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 

  72. 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 

  73. 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 

  74. 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 

  75. 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 

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

    Google Scholar 

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

    Book  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Book  Google Scholar 

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

    Google Scholar 

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

    Book  Google Scholar 

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

    Book  Google Scholar 

  84. 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 

  85. 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 

  86. 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 

  87. 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 

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

    Google Scholar 

  89. 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 

  90. W. Liu, W. Gao, X. Mu, Fast inter-frame motion prediction for compressed dynamic point cloud attribute enhancement. Proc. AAAI Conf. Artif. Intell. 38(4), 3720–3728 (2024)

    Google Scholar 

  91. 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 

  92. 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 

  93. 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 

  94. 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 

  95. 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 Sens. 60, 1–14 (2022)

    Google Scholar 

  96. 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 

  97. 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 

  98. 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 

  99. 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 

  100. 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. Comput. Visual Media (2024)

    Google Scholar 

  101. J. Wang, W. Gao, G. Li, Applying collaborative adversarial learning to blind point cloud quality measurement. IEEE Trans. Instrum. Measure. 72, 1–15 (2023)

    Google Scholar 

  102. 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 

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

    Google Scholar 

  104. 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 

  105. X. Lu, 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 

  106. Z. Pan, N. Zhang, W. Gao, S. Liu, G. Li, Less is more: label recommendation for weakly supervised point cloud semantic segmentation. Proc. AAAI Conf. Artif. Intell. 38(5) 4397–4405 (2024)

    Google Scholar 

  107. 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 

  108. 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 

  109. 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 

  110. 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 

  111. M.A. Uy, G.H. Lee, PointNetVLAD: deep point cloud based retrieval for large-scale place recognition, in IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 4470–4479

    Google Scholar 

  112. J. Komorowski, MinkLoc3D: point cloud based large-scale place recognition, in IEEE Winter Conference on Applications of Computer Vision (2021), pp. 1789–1798

    Google Scholar 

  113. L. Hui, H. Yang, M. Cheng, J. Xie, J. Yang, Pyramid point cloud transformer for large-scale place recogition, in IEEE Conference on Computer Vision and Pattern Recognition (2021), pp. 6078–6087

    Google Scholar 

  114. R. Zhang, G. Li, W. Gao, T.H. Li, Compoint: can complex-valued representation benefit point cloud place recognition? IEEE Trans. Intell. Transport. Syst. 25(7), 7494–7507 (2024)

    Article  Google Scholar 

  115. S.B. Hegde, S. Gangisetty, An evaluation of feature encoding techniques for non-rigid and rigid 3d point cloud retrieval, in British Machine Vision Conference (2019), p. 47

    Google Scholar 

  116. W. Zhang, C. Xiao, PCAN: 3d attention map learning using contextual information for point cloud based retrieval, in IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 12 436–12 445

    Google Scholar 

  117. Q. Sun, H. Liu, J. He, Z. Fan, X. Du, DAGC: employing dual attention and graph convolution for point cloud based place recognition, in International Conference on Multimedia Retrieval (2020), pp. 224–232

    Google Scholar 

  118. C.R. Qi, H. Su, K. Mo, L.J. Guibas, PointNet: deep learning on point sets for 3D classification and segmentation, in IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 77–85

    Google Scholar 

  119. C. Choy, J. Gwak, S. Savarese, 4d spatio-temporal convnets: minkowski convolutional neural networks, in IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 3075–3084

    Google Scholar 

  120. F. Radenovic, G. Tolias, O. Chum, Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655–1668 (2019)

    Article  Google Scholar 

  121. T. Lin, P. Dollár, R.B. Girshick, K. He, B. Hariharan, S.J. Belongie, Feature pyramid networks for object detection, in IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, Washington, 2017), pp. 936–944

    Google Scholar 

  122. J. Komorowski, M. Wysoczanska, T. Trzcinski, Minkloc++: Lidar and monocular image fusion for place recognition, in International Joint Conference on Neural Networks (IEEE, Piscataway, 2021), pp. 1–8

    Google Scholar 

  123. Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, Q. Hu, ECA-Net: efficient channel attention for deep convolutional neural networks, in IEEE Conference on Computer Vision and Pattern Recognition (2020), pp. 11 531–11 539

    Google Scholar 

  124. W. Maddern, G. Pascoe, C. Linegar, P. Newman, 1 year, 1000 km: the Oxford robotcar dataset. Int. J. Robot. Res. 36(1), 3–15 (2017)

    Article  Google Scholar 

  125. X. Huang, G. Mei, J. Zhang, R. Abbas, A comprehensive survey on point cloud registration. CoRR, vol. abs/2103.02690, 2021. [Online]. Available: https://arxiv.org/abs/2103.02690

  126. P.J. Besl, n.d. McKay, A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Google Scholar 

  127. L. Cheng, S. Chen, X. Liu, H. Xu, Y. Wu, M. Li, Y. Chen, Registration of laser scanning point clouds: a review. Sensors 18(5), 1641 (2018)

    Google Scholar 

  128. H.M. Le, T. Do, T. Hoang, N. Cheung, SDRSAC: semidefinite-based randomized approach for robust point cloud registration without correspondences, in IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 124–133

    Google Scholar 

  129. F. Pomerleau, F. Colas, R. Siegwart, A review of point cloud registration algorithms for mobile robotics, Found. Trends Robot. 4(1), 1–104 (2015)

    Article  Google Scholar 

  130. H. Yang, L. Carlone, A polynomial-time solution for robust registration with extreme outlier rates, in Robotics: Science and Systems XV, University of Freiburg, Freiburg im Breisgau, June 22–26, 2019, ed. by A. Bicchi, H. Kress-Gazit, S. Hutchinson (2019)

    Google Scholar 

  131. H. Deng, T. Birdal, S. Ilic, PPFNet: Global context aware local features for robust 3d point matching, in IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 195–205

    Google Scholar 

  132. Z. Gojcic, C. Zhou, J.D. Wegner, A. Wieser, The perfect match: 3d point cloud matching with smoothed densities, in IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 5545–5554

    Google Scholar 

  133. A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, T.A. Funkhouser, 3DMatch: learning local geometric descriptors from RGB-D reconstructions, in IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 199–208

    Google Scholar 

  134. G. Elbaz, T. Avraham, A. Fischer, 3d point cloud registration for localization using a deep neural network auto-encoder, in IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2472–2481

    Google Scholar 

  135. W. Lu, G. Wan, Y. Zhou, X. Fu, P. Yuan, S. Song, DeepVCP: an end-to-end deep neural network for point cloud registration, in IEEE/CVF International Conference on Computer Vision (IEEE, Piscataway, 2019), pp. 12–21

    Google Scholar 

  136. Z. Yang, J.Z. Pan, L. Luo, X. Zhou, K. Grauman, Q. Huang, Extreme relative pose estimation for RGB-D scans via scene completion, in IEEE Conference on Computer Vision and Pattern Recognition (2019), pp. 4531–4540

    Google Scholar 

  137. X. Huang, L. Fan, Q. Wu, J. Zhang, C. Yuan, Fast registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement, in IEEE International Conference on Multimedia and Expo (2019), pp. 1552–1557

    Google Scholar 

  138. X. Huang, J. Zhang, L. Fan, Q. Wu, C. Yuan, A systematic approach for cross-source point cloud registration by preserving macro and micro structures. IEEE Trans. Image Proces. 26(7), 3261–3276 (2017)

    Article  MathSciNet  Google Scholar 

  139. X. Huang, J. Zhang, Q. Wu, L. Fan, C. Yuan, A coarse-to-fine algorithm for registration in 3d street-view cross-source point clouds, in International Conference on Digital Image Computing: Techniques and Applications (2016), pp. 1–6

    Google Scholar 

  140. X. Huang, G. Mei, J. Zhang, Feature-metric registration: a fast semi-supervised approach for robust point cloud registration without correspondences, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, June 13–19, 2020 (Computer Vision Foundation/IEEE, Piscataway, 2020), pp. 11 363–11 371

    Google Scholar 

  141. 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 (IEEE Computer Society, Washington, 2015), pp. 1912–1920

    Google Scholar 

  142. A. Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)

    Article  Google Scholar 

  143. 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 

  144. Y. Zhou, O. Tuzel, VoxelNet: end-to-end learning for point cloud based 3d object detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 4490–4499

    Google Scholar 

  145. M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, F. Heide, Seeing through fog without seeing fog: deep multimodal sensor fusion in unseen adverse weather, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 11 682–11 692

    Google Scholar 

  146. J.H. Yoo, Y. Kim, J. Kim, J.W. Choi, 3D-CVF: generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection, in European Conference on Computer Vision (2020), pp. 720–736

    Google Scholar 

  147. L. Xie, G. Xu, D. Cai, X. He, X-view: non-egocentric multi-view 3d object detector. IEEE Trans. Image Proces. 32, 1488–1497 (2023)

    Article  Google Scholar 

  148. K. Huang, B. Shi, X. Li, X. Li, S. Huang, Y. Li, Multi-modal sensor fusion for auto driving perception: a survey. Preprint. arXiv:2202.02703 (2022)

    Google Scholar 

  149. S. Vora, A. H. Lang, B. Helou, O. Beijbom, Pointpainting: sequential fusion for 3d object detection, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 4604–4612

    Google Scholar 

  150. L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai, X. He, PI-RCNN: an efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module. Proc. AAAI Conf. Artif. Intell. 34(07), 12 460–12 467 (2020)

    Google Scholar 

  151. T. Huang, Z. Liu, X. Chen, X. Bai, EPNet: enhancing point features with image semantics for 3d object detection, in European Conference on Computer Vision (2020), pp. 35–52

    Google Scholar 

  152. M. Liang, B. Yang, S. Wang, R. Urtasun, Deep continuous fusion for multi-sensor 3d object detection, in Proceedings of the European Conference on Computer Vision (2018), pp. 641–656

    Google Scholar 

  153. S. Pang, D. Morris, H. Radha, CLOCs: camera-lidar object candidates fusion for 3d object detection, in IEEE/RSJ International Conference on Intelligent Robots and Systems (2020), pp. 10 386–10 393

    Google Scholar 

  154. C.R. Qi, W. Liu, C. Wu, H. Su, L.J. Guibas, Frustum pointnets for 3d object detection from RGB-D data, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 918–927

    Google Scholar 

  155. P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, et al., Scalability in perception for autonomous driving: Waymo open dataset, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 2446–2454

    Google Scholar 

  156. H. Caesar, V. Bankiti, A.H. Lang, S. Vora, V.E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, O. Beijbom, nuScenes: a multimodal dataset for autonomous driving, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 11 621–11 631

    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). Deep-Learning-Based Point Cloud Analysis II. In: Deep Learning for 3D Point Clouds. Springer, Singapore. https://doi.org/10.1007/978-981-97-9570-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-9570-3_6

  • 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