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@article{188581,
        author = {Likhitha H Y and Tejaswi H T and Thejaswini L Gowda and Muskan and Mrs. Megha H C},
        title = {The Explicit Frontier: A Comparative Analysis of 3D Gaussian Splatting and the Quest for Real-Time Radiance Field Rendering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4762-4769},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188581},
        abstract = {The advent of Neural Radiance Fields (NeRF) inaugurated a period of rapid advancement in photorealistic novel view synthesis, defining complex scenes as continuous volumetric functions. However, NeRF’s computational demands—requiring hundreds of Multilayer Perceptron (MLP) queries per ray—rendered it fundamentally unsuitable for real-time applications. This report conducts a rigorous comparison of the three primary architectural movements developed to address this computational bottleneck: volumetric baking via the Sparse Neural Radiance Grid (SNeRG), implicit acceleration through multi-resolution hashing (Instant Neural Graphics Primitives, Instant-NGP), and the explicit representation paradigm shift, 3D Gaussian Splatting (3DGS). The analysis demonstrates that while Instant-NGP achieved breakthrough improvements in training speed and interactive rendering, 3DGS, through its utilization of optimized explicit primitives and fast differentiable rasterization, achieves superior fidelity, hyper-real-time rendering speeds, and enhanced practicality, thereby establishing the explicit representation as the current state-of-the-art architecture for deployment in interactive graphics pipelines.},
        keywords = {},
        month = {December},
        }