{"ID":2864068,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23555","arxiv_id":"2509.23555","title":"From Fields to Splats: A Cross-Domain Survey of Real-Time Neural Scene Representations","abstract":"Neural scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have transformed how 3D environments are modeled, rendered, and interpreted. NeRF introduced view-consistent photorealism via volumetric rendering; 3DGS has rapidly emerged as an explicit, efficient alternative that supports high-quality rendering, faster optimization, and integration into hybrid pipelines for enhanced photorealism and task-driven scene understanding. This survey examines how 3DGS is being adopted across SLAM, telepresence and teleoperation, robotic manipulation, and 3D content generation. Despite their differences, these domains share common goals: photorealistic rendering, meaningful 3D structure, and accurate downstream tasks. We organize the review around unified research questions that explain why 3DGS is increasingly displacing NeRF-based approaches: What technical advantages drive its adoption? How does it adapt to different input modalities and domain-specific constraints? What limitations remain? By systematically comparing domain-specific pipelines, we show that 3DGS balances photorealism, geometric fidelity, and computational efficiency. The survey offers a roadmap for leveraging neural rendering not only for image synthesis but also for perception, interaction, and content creation across real and virtual environments.","short_abstract":"Neural scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have transformed how 3D environments are modeled, rendered, and interpreted. NeRF introduced view-consistent photorealism via volumetric rendering; 3DGS has rapidly emerged as an explicit, efficient alternative that supp...","url_abs":"https://arxiv.org/abs/2509.23555","url_pdf":"https://arxiv.org/pdf/2509.23555v1","authors":"[\"Javed Ahmad\",\"Penggang Gao\",\"Donatien Delehelle\",\"Mennuti Canio\",\"Nikhil Deshpande\",\"Jesús Ortiz\",\"Darwin G. Caldwell\",\"Yonas Teodros Tefera\"]","published":"2025-09-28T01:30:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}