The goal of this paper is to encode a 3D scene into an extremely compact representation from 2D images and to enable its transmittance, decoding and rendering in real-time across various platforms. Despite the progress in NeRFs and Gaussian Splats, their large model size and specialized renderers make it challenging to distribute free-viewpoint 3D content as easily as images. To address this, we have designed a novel 3D representation that encodes the plenoptic function into sinusoidal function indexed dense volumes. This approach facilitates feature sharing across different locations, improving compactness over traditional spatial voxels. The memory footprint of the dense 3D feature grid can be further reduced using spatial decomposition techniques. This design combines the strengths of spatial hashing functions and voxel decomposition, resulting in a model size as small as 150 KB for each 3D scene. Moreover, PPNG features a lightweight rendering pipeline with only 300 lines of code that decodes its representation into standard GL textures and fragment shaders. This enables real-time rendering using the traditional GL pipeline, ensuring universal compatibility and efficiency across various platforms without additional dependencies.
本文的目标是从二维图像中对三维场景进行极为紧凑的表示编码，并使其能够在多个平台上实时传输、解码和渲染。尽管NeRF和高斯分布在这一领域取得了进展，但其较大的模型尺寸和专门的渲染器使得像图像一样轻松分发自由视角3D内容变得具有挑战性。为了解决这一问题，我们设计了一种新颖的三维表示方法，将全光函数编码为以正弦函数索引的密集体素。该方法有助于在不同位置共享特征，相较于传统的空间体素提高了紧凑性。通过使用空间分解技术，可以进一步减少密集三维特征网格的内存占用。此设计结合了空间哈希函数和体素分解的优势，使每个三维场景的模型大小可缩小至150 KB。此外，PPNG 具有一个轻量级的渲染管道，仅需300行代码即可将其表示解码为标准GL纹理和片段着色器。这使得使用传统GL管道进行实时渲染成为可能，确保了各种平台上的通用兼容性和高效性，无需额外的依赖。