比较两种神经辐射场流媒体策略的效率与质量平衡
Evaluation of strategies for efficient rate-distortion NeRF streaming
- 按像素编码传输或直接传神经网络参数，对比流媒体效果
- 参数传输策略在多数情况下更高效，适合一对多分发
- 揭示了复杂度与画质之间的权衡，指导实际部署选择
神经辐射场（NeRF）通过从稀疏图像集重建高保真、细节丰富的三维场景，革新了3D视觉表示领域。其采用体素函数表示法，将3D点映射为颜色和不透明度，实现任意视角下的逼真图像合成。尽管如此，由于数据量庞大，NeRF内容的高效流媒体传输仍是重大挑战。本文研究了两种流媒体策略的率失真性能：基于像素的传输和基于神经网络参数的传输。前者对图像编码后传输，后者则直接编码并传输对应的NeRF模型参数。研究揭示了两者在复杂度与性能间的权衡，表明参数传输策略通常具有更高的效率，更适合一对多的流媒体场景。
原文摘要 · Abstract (English)
Neural Radiance Fields (NeRF) have revolutionized the field of 3D visual representation by enabling highly realistic and detailed scene reconstructions from a sparse set of images. NeRF uses a volumetric functional representation that maps 3D points to their corresponding colors and opacities, allowing for photorealistic view synthesis from arbitrary viewpoints. Despite its advancements, the efficient streaming of NeRF content remains a significant challenge due to the large amount of data involved. This paper investigates the rate-distortion performance of two NeRF streaming strategies: pixel-based and neural network (NN) parameter-based streaming. While in the former, images are coded and then transmitted throughout the network, in the latter, the respective NeRF model parameters are coded and transmitted instead. This work also highlights the trade-offs in complexity and performance, demonstrating that the NN parameter-based strategy generally offers superior efficiency, making it suitable for one-to-many streaming scenarios.
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