Dynamic Neural Radiance Fields (Dynamic NeRF) enhance NeRF technology to model moving scenes. However, they are resource intensive and challenging to compress. To address these issues, this paper presents WavePlanes, a fast and more compact explicit model. We propose a multi-scale space and space-time feature plane representation using N-level 2-D wavelet coefficients. The inverse discrete wavelet transform reconstructs feature signals at varying detail, which are linearly decoded to approximate the color and density of volumes in a 4-D grid. Exploiting the sparsity of wavelet coefficients, we compress the model using a Hash Map containing only non-zero coefficients and their locations on each plane. Compared to the state-of-the-art (SotA) plane-based models, WavePlanes is up to 15x smaller while being less resource demanding and competitive in performance and training time. Compared to other small SotA models WavePlanes preserves details better without requiring custom CUDA code or high performance computing resources. Our code is available at: https://github.com/azzarelli/waveplanes/

翻译：动态神经辐射场（Dynamic NeRF）增强了NeRF技术对运动场景的建模能力。然而，此类模型资源消耗大且难以压缩。为解决上述问题，本文提出WavePlanes——一种快速且更紧凑的显式模型。我们利用N级二维小波系数构建多尺度空间及时空特征平面表示。通过逆离散小波变换在不同细节层次上重构特征信号，再经线性解码近似得到四维网格中体素的颜色与密度。利用小波系数的稀疏性，我们采用哈希映射仅存储非零系数及其在各平面的位置，从而实现模型压缩。与当前最先进的平面基模型相比，WavePlanes体积缩小达15倍，且资源需求更低，在性能与训练时间上具备竞争力。相较于其他小型最优模型，WavePlanes无需自定义CUDA代码或高性能计算资源即可更好地保留细节。我们的代码开源在：https://github.com/azzarelli/waveplanes/