*Result*: NeRF-Optimized Signal Encoding and Vision-Based 3D Reconstruction for Real-Time Semantic Scene Understanding.

*Neural Radiance Fields (NeRF) have significantly advanced 3D scene reconstruction by enabling photorealistic rendering from sparse 2D image collections through deep implicit representations. However, their high computational cost remains a major barrier to real-time deployment in dynamic environments. In this work, we propose a novel NeRF-driven framework tailored for real-time 3D object and scene understanding. Our method integrates a hybrid implicit-explicit encoding scheme and an optimized ray sampling strategy to effectively reduce inference latency while preserving geometric fidelity. Beyond acceleration, the framework incorporates semantic-level scene parsing, allowing for real-time object interaction and contextual understanding. Extensive experiments validate our approach across multiple benchmarks, demonstrating improved reconstruction speed and semantic accuracy compared to baseline NeRF variants. This work bridges the gap between high-quality neural rendering and the demands of real-world intelligent systems such as robotics, augmented reality, and autonomous perception. [ABSTRACT FROM AUTHOR]

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