CWC-DNERF: Compact Dynamic Neural Radiance Field VIA Discrete Wavelet Transform And Learnable Codebooks

Neural radiance fields have significantly advanced dynamic scene reconstruction and novel view synthesis. However, relying on multiple implicit multi-layer perceptrons for reconstructing dynamic scenes is computationally expensive. Recent methods have alleviated this challenge by introducing explicit data structures, such as voxel grids and feature planes, but these significantly increase storage demands and complicate network transmission. We propose Cwc-DNeRF, a compact dynamic NeRF representation that leverages discrete wavelet transform (DWT) and learnable codebooks to achieve superior storage efficiency while maintaining competitive rendering quality compared to K-Planes. In Stage I, DWT and trainable masks are employed to optimize parameter efficiency, resulting in sparse space planes. In Stage II, learnable codebooks are introduced for the space-time planes to merge redundant spatio-temporal features further reducing the storage demand. Additionally, a data compression pipeline is applied to compress both sparse space plane parameters and codebooks. Experimental results on D-NeRF and DyNeRF datasets show that our method achieves state-of-the-art rendering quality within a 10MB storage budget while retaining the benefits of explicit feature planes.