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[2602.17124] 3D Scene Rendering with Multimodal Gaussian Splatting
Summary
This paper presents a novel approach to 3D scene rendering using multimodal Gaussian splatting, integrating RF sensing for improved accuracy and efficiency in challenging conditions.
Why It Matters
The integration of RF sensing with Gaussian splatting represents a significant advancement in 3D rendering technology. This approach addresses limitations of traditional vision-based methods, particularly in adverse conditions, making it highly relevant for applications in robotics and autonomous driving.
Key Takeaways
- Multimodal Gaussian splatting enhances 3D scene rendering accuracy.
- RF sensing provides robustness against adverse environmental conditions.
- The method reduces reliance on extensive camera views for initialization.
- Numerical tests validate the effectiveness of the proposed framework.
- This approach has potential applications in industrial monitoring and autonomous systems.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.17124 (cs) [Submitted on 19 Feb 2026] Title:3D Scene Rendering with Multimodal Gaussian Splatting Authors:Chi-Shiang Gau, Konstantinos D. Polyzos, Athanasios Bacharis, Saketh Madhuvarasu, Tara Javidi View a PDF of the paper titled 3D Scene Rendering with Multimodal Gaussian Splatting, by Chi-Shiang Gau and 4 other authors View PDF HTML (experimental) Abstract:3D scene reconstruction and rendering are core tasks in computer vision, with applications spanning industrial monitoring, robotics, and autonomous driving. Recent advances in 3D Gaussian Splatting (GS) and its variants have achieved impressive rendering fidelity while maintaining high computational and memory efficiency. However, conventional vision-based GS pipelines typically rely on a sufficient number of camera views to initialize the Gaussian primitives and train their parameters, typically incurring additional processing cost during initialization while falling short in conditions where visual cues are unreliable, such as adverse weather, low illumination, or partial occlusions. To cope with these challenges, and motivated by the robustness of radio-frequency (RF) signals to weather, lighting, and occlusions, we introduce a multimodal framework that integrates RF sensing, such as automotive radar, with GS-based rendering as a more efficient and robust alternative to vision-only GS rendering. The proposed approach enables efficient depth predi...