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Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving (2402.14642v1)

Published 22 Feb 2024 in cs.CV and cs.DC

Abstract: The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more. DTs can be created with 3D scene reconstruction methods that capture the real world's geometry, appearance, and dynamics. However, sending data for real-time DT updates in the metaverse, such as camera images and videos from connected autonomous vehicles (CAVs) to edge servers, can increase network congestion, costs, and latency, affecting metaverse services. Herein, a new method is proposed based on distributed radiance fields (RFs), multi-access edge computing (MEC) network for video compression and metaverse DT updates. RF-based encoder and decoder are used to create and restore representations of camera images. The method is evaluated on a dataset of camera images from the CARLA simulator. Data savings of up to 80% were achieved for H.264 I-frame - P-frame pairs by using RFs instead of I-frames, while maintaining high peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) qualitative metrics for the reconstructed images. Possible uses and challenges for the metaverse and autonomous mobility are also discussed.

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References (18)
  1. H. Alves, G. D. Jo, J. Shin, C. Yeh, N. H. Mahmood, C. H. M. de Lima, C. Yoon, G. Park, N. Rahatheva, O.-S. Park, and et al., “Beyond 5G urllc evolution: New service modes and practical considerations,” ITU Journal on Future and Evolving Technologies, vol. 3, no. 3, p. 545–554, 2022.
  2. E. Glaessgen and D. Stargel, “The digital twin paradigm for future NASA and U.S. air force vehicles,” 04 2012.
  3. M. Xu, W. C. Ng, W. Y. B. Lim, J. Kang, Z. Xiong, D. Niyato, Q. Yang, X. Shen, and C. Miao, “A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges,” IEEE Communications Surveys and Tutorials, vol. 25, no. 1, pp. 656–700, 2023.
  4. S. Mihai, M. Yaqoob, D. V. Hung, W. Davis, P. Towakel, M. Raza, M. Karamanoglu, B. Barn, D. Shetve, R. V. Prasad, H. Venkataraman, R. Trestian, and H. X. Nguyen, “Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects,” IEEE Communications Surveys and Tutorials, vol. 24, no. 4, pp. 2255–2291, 2022.
  5. Y. Ren, R. Xie, F. R. Yu, T. Huang, and Y. Liu, “Quantum Collective Learning and Many-to-Many Matching Game in the Metaverse for Connected and Autonomous Vehicles,” IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12 128–12 139, 2022.
  6. M. Xu, D. Niyato, H. Zhang, J. Kang, Z. Xiong, S. Mao, and Z. Han, “Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse,” 2023.
  7. P. Zhou, J. Zhu, Y. Wang, Y. Lu, Z. Wei, H. Shi, Y. Ding, Y. Gao, Q. Huang, Y. Shi, A. Alhilal, L.-H. Lee, T. Braud, P. Hui, and L. Wang, “Vetaverse: A Survey on the Intersection of Metaverse, Vehicles, and Transportation Systems,” 2023.
  8. Y. Zhang, T. van Rozendaal, J. Brehmer, M. Nagel, and T. Cohen, “Implicit neural video compression,” arXiv preprint arXiv:2112.11312, 2021.
  9. Z. Chen, G. Lu, Z. Hu, S. Liu, W. Jiang, and D. Xu, “LSVC: A learning-based stereo video compression framework,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6073–6082.
  10. D. Ding, Z. Ma, D. Chen, Q. Chen, Z. Liu, and F. Zhu, “Advances in video compression system using deep neural network: A review and case studies,” Proceedings of the IEEE, vol. 109, no. 9, pp. 1494–1520, 2021.
  11. R. Birman, Y. Segal, and O. Hadar, “Overview of research in the field of video compression using deep neural networks,” Multimedia Tools and Applications, vol. 79, pp. 11 699–11 722, 2020.
  12. M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar, “Block-NeRF: Scalable large scene neural view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 8248–8258.
  13. Y. Liu, X. Tu, D. Chen, K. Han, O. Altintas, H. Wang, and J. Xie, “Visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges,” in 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS).   IEEE, 2023, pp. 170–177.
  14. Z. Wu, T. Liu, L. Luo, Z. Zhong, J. Chen, H. Xiao, C. Hou, H. Lou, Y. Chen, R. Yang et al., “Mars: An instance-aware, modular and realistic simulator for autonomous driving,” arXiv preprint arXiv:2307.15058, 2023.
  15. A. Byravan, J. Humplik, L. Hasenclever, A. Brussee, F. Nori, T. Haarnoja, B. Moran, S. Bohez, F. Sadeghi, B. Vujatovic et al., “Nerf2real: Sim2real transfer of vision-guided bipedal motion skills using neural radiance fields,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 9362–9369.
  16. B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM, vol. 65, no. 1, pp. 99–106, 2021.
  17. T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Transactions on Graphics (ToG), vol. 41, no. 4, pp. 1–15, 2022.
  18. B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3D Gaussian Splatting for Real-Time Radiance Field Rendering,” 2023.
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Authors (5)
  1. Eugen Šlapak (4 papers)
  2. Matúš Dopiriak (4 papers)
  3. Mohammad Abdullah Al Faruque (51 papers)
  4. Juraj Gazda (5 papers)
  5. Marco Levorato (50 papers)
Citations (1)
View on Semantic Scholar

Summary

  • The paper presents a novel RF-based encoding and decoding approach that achieves up to 80% data compression while maintaining high PSNR and SSIM metrics.
  • It leverages distributed radiance fields to reconstruct 3D scenes from sparse 2D images using camera pose data for efficient edge processing.
  • Experiments on the CARLA simulator demonstrate reduced network congestion and enable real-time updates for autonomous driving and metaverse applications.

Distributed Radiance Fields for Enhanced Video Compression in Autonomous Mobility

Introduction to Radiance Fields and Edge Computing

Connected autonomous vehicles (CAVs) have become increasingly common, generating substantial data from various sensors. This data, essential for real-time decision-making and immersive experiences in the metaverse, poses significant challenges regarding network congestion, costs, and latency. Multi-access edge computing (MEC) offers a solution by offloading data and computational tasks to edge servers. However, even with MEC, the demand for advanced data compression techniques is undeniable. The paper introduces a novel approach leveraging distributed radiance fields (RFs) for video compression and metaverse digital twins (DTs) updates, significantly reducing data transmission requirements while maintaining high-quality image reconstructions.

Advances in Video Compression

The traditional video compression schemes are dependent on optical flow methods, which have limitations, especially in dynamic environments seen by CAVs. In contrast, distributed RFs offer a structured understanding of the 3D scene, allowing for more efficient data compression. The paper suggests an RF-based encoder and decoder methodology that uses a sparse set of 2D images to reconstruct 3D scenes and subsequently compress video data, offering potential data savings of up to 80% compared to conventional methods like the H.264 codec, without compromising the quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Neural Radiance Fields (NeRF)

NeRFs encode 3D scenes into a neural network, representing scenes in a compact form and allowing for the reconstruction of any camera view within the scene. By training on a set of 2D images, NeRFs can reproduce complex scenes with high fidelity. The paper utilizes Radiance Fields for encoding and decoding video frames, achieving significant compression by eliminating the need for transmitting complete frame information over the network.

Methodology and Results

The proposed methodology employs RF-based encoding and decoding, where the encoder uses camera poses to render and encode scene differences, and the decoder reconstructs the original image using the encoded differences and stored RF. This approach was validated using the CARLA simulator, showing significant compression savings across various scenarios without sacrificing image quality. The experimental results indicated that the RF-based method achieved substantial data savings and high-quality reconstructions compared to traditional methods.

Implications and Future Directions

The research presents a significant advancement in data compression techniques, with profound implications for the future of autonomous mobility and metaverse applications. By decreasing the data transmission requirements, the approach facilitates more efficient and scalable deployment of CAVs and immersive metaverse experiences. Looking ahead, integrating RF-based video compression with existing edge computing frameworks could further enhance the performance of autonomous systems and metaverse platforms, offering real-time updates with minimal latency and high fidelity.

Conclusion

This paper introduces a pioneering approach for video compression and metaverse updates in the context of autonomous driving, using distributed RFs for efficient data encoding. The methodology shows promising results for reducing network congestion and enabling rapid, high-quality updates for DTs in the metaverse, setting the stage for future innovations in autonomous mobility and digital twin technologies.

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