ACEsplat - Accelerated 3D Gaussian Scene Regression via RGB and Poses Only: Deployment and Systems Integration

Operational value depends on latency, resource use, interface dependencies, and reliability within the systems that must host the method. This structured evidence review evaluates "ACEsplat: Accelerated 3D Gaussian Scene Regression via RGB and Poses Only" alongside nine author-disjoint, topically matched publications in resource-efficient autonomous perception. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through deployment constraints and systems integration, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda tests end-to-end latency, failure recovery, resource demand, and compatibility with existing operational interfaces.

1. Liu, M., Que, H., Fan, D., Gao, H., Zhu, T., Yao, H., Zhang, Q., Zhang, R., Huang, X., & Qiao, F. (2026). ACEsplat: Accelerated 3D Gaussian Scene Regression via RGB and Poses Only. arXiv. https://doi.org/10.48550/arXiv.2606.22091 DOI
2. Costa, B.-T., Pereira, C., & Maykol Pinto, A. (2025). PerceptNet-V2X: Perception Network for Vehicle to Everything Scenarios in Autonomous Driving. IEEE Access, 13, 182645-182660. https://doi.org/10.1109/access.2025.3624285 DOI
3. Chandrashekhar, B., & Shakkeera, L. (2025). Reinforcement learning in Autonomous vehicle communication under V2X Framework using edge computing and Privacy-Preserving AI. 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS), 1-8. https://doi.org/10.1109/iccams65118.2025.11234562 DOI
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5. Asaju, B.-J., Jung, W., & Wakili, A. (2025). A Survey of Multi-Layered Cybersecurity Threats in Connected and Autonomous Vehicles: Risks from Edge Computing and V2x Protocols. . https://doi.org/10.2139/ssrn.5260375 DOI
6. Garcia, C.-E., Camana, M.-R., Mohammad, A.-B., Querol, J., & Chatzinotas, S. (2024). Edge Learning Optimization in Task-Oriented NOMA Communications for Autonomous Vehicle Perception. 2024 IEEE Wireless Communications and Networking Conference (WCNC), 1-6. https://doi.org/10.1109/wcnc57260.2024.10570514 DOI
7. Wang, S. (2025). Edge Intelligence for V2X Communications: Advances in Collaborative Perception and Privacy Preservation. Journal of Big Data and Computing, 3(4), 73-81. https://doi.org/10.62517/jbdc.202501409 DOI
8. Richards, E., Thapa, B., & Mashayekhy, L. (2025). Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception. 2025 IEEE International Conference on Edge Computing and Communications (EDGE), 13-22. https://doi.org/10.1109/edge67623.2025.00011 DOI
9. Roy, A. (2025). Beyond V2X: A Review of Large Language Models in In-Vehicle Autonomous Driving Systems. . https://doi.org/10.36227/techrxiv.175571972.28517288/v1 DOI
10. Soorchaei, B.-E., Raftari, A., & Fallah, Y.-P. (2025). Extensible Heterogeneous Collaborative Perception in Autonomous Vehicles with Codebook Compression. Robotics, 14(12), 186. https://doi.org/10.3390/robotics14120186 DOI