# Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving

arXiv: https://arxiv.org/abs/2402.14642 · HTML: https://mlsanity.com/paper/2402.14642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving](https://mlsanity.com/paper/2307.15058/) (2023)
- [Implicit Neural Video Compression](https://mlsanity.com/paper/2112.11312/) (2021)
- [NeRF2Real: Sim2real Transfer of Vision-guided Bipedal Motion Skills using Neural Radiance Fields](https://mlsanity.com/paper/2210.04932/) (2022)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Advances In Video Compression System Using Deep Neural Network: A Review And Case Studies](https://mlsanity.com/paper/2101.06341/) (2021)
- [Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse](https://mlsanity.com/paper/2301.07636/) (2023)
- [Beyond 5G URLLC Evolution: New Service Modes and Practical Considerations](https://mlsanity.com/paper/2106.11825/) (2021)
- [Vetaverse: A Survey on the Intersection of Metaverse, Vehicles, and Transportation Systems](https://mlsanity.com/paper/2210.15109/) (2022)
- [Visualization of Mobility Digital Twin: Framework Design, Case Study, and Future Challenges](https://mlsanity.com/paper/2307.09666/) (2023)

## Abstract (arXiv)

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.