The field of 3D shape generation has recently seen a surge in interest, with researchers exploring various techniques to create high-dimensional 3D shapes. Traditionally, the shape generation community has relied on encoding 3D shapes into low-dimensional latent spaces using trained encoder models, and then synthesizing new shapes using latent generative models. However, this approach has its limitations, as it requires a trained encoder, which may not be optimal. In contrast, this work proposes an innovative integration of wavelet decomposition and Singular Value Decomposition (SVD) as a learning-free approach to capturing the essence of 3D shapes. The proposed method embeds 3D shapes into a robust, differentiable implicit representation without the need for a prior learning phase. This learning-free approach allows for efficient mesh embedding, transforming a 15,000-vertex mesh into a compact 512-dimensional latent code. The key advantage of this representation is its suitability for scenarios with limited data, where conventional generative models may struggle. By leveraging a score- matching generative model, the proposed pipeline demonstrates the ability to construct high-quality 3D shapes that are comparable to state-of-the-art methods while reducing the dependency on large-scale datasets, which are often a bottleneck in synthesizing complex 3D geometries.