Optimizing Neural Radiance Field with Volume Density Regularization

Neural Radiance Fields have emerged as a powerful framework for novel view synthesis, owing to its ability to represent scenes with high fidelity. However, its performance can often decline when reconstructing high-frequency details such as textures and fine edges. To address this limitation, we propose a volume density regularization strategy that includes ray termination regularization and foreground suppression regularization. The first component ensures the volume densities peak near the ray termination surface, while the second enforces that volume densities are approximately zero in front of the scene. Extensive experiments demonstrate that our method outperforms Vanilla NeRF and other enhanced variants in terms of scene reconstruction quality.