In pursuit of alleviating the shortcomings of the Neural Radiance Fields (NeRF) technique for three-dimensional scene reconstruction and novel view synthesis, this work introduces a novel approach by synergizing multi-resolution hash encoding with an L0-Sampler sampling strategy within the Mip-NeRF framework. The hashed representations enable efficient encodings of scenes across multiple scales, circumventing the limitations of fidelity at varying resolutions. Concurrently, the L0-Sampler optimizes sample distribution by gravitating closer to object surfaces, notably enhancing rendered image quality while curtailing training durations. Upheld by rigorous experimentation on diverse datasets, the augmented model conspicuously surpasses prevailing NeRF-derived methods with a remarkable 21% plummet in error rates and quintupling of training velocities. These advances are corroborated by quantitative benchmarks and visual exemplifications, demonstrating substantive progressions in view reconstruction capabilities.