TernaryNeRF: Quantizing Voxel Grid-based NeRF Models

Photo-realistic neural rendering, represented by neural radiance field (NeRF), is considered to be a key technology for AR/VR applications and has been actively studied in recent years. In order to enable widespread adoptions of AR/VR, it is critical to enable low-cost and high-quality rendering on mobile and server systems. In our work, we investigate the feasibility of low-precision representation on the two state-of-the-art NeRF models, InstantNeRF and TensoRF. Our proposed quantization is based on our observation on the characteristics of trained NeRF models. In order to reduce the model size while limiting the loss of rendering quality due to model compression, we propose quantizing the portion of model which dominates the total model size while being robust to aggressive quantization. In our experiments, we demonstrate our proposed ternary quantization can reduce by $7 \times \sim 15\times$ the model sizes of state-of-the-art NeRF models at a negligible loss of rendering quality, which, we consider, will contribute to the AR/VR adoptions on mobile and server systems.