Latent Multi-Scale Diffusion Model for Neural Implicit 3D Shape Generation

This paper proposes a novel method for neural implicit 3D shape synthesis using a two-stage training strategy with an implicit autoencoder and a multi-scale denoising diffusion model. This presented approach is built on autoencoding implicit fields, performed multi-scale denoising diffusion process on the latent implicit grids encoded by our implicit autoencoder. To be specific, those latent implicit grids maintain the position awareness trait compared to traditional global implicit codes so that generating 3D shapes with more local details are further allowed when implementing our designed multi-scale denoising diffusion process. The approach’s effectiveness of shape reconstruction and synthesis, demonstrated on the ShapeNet dataset, make it a promising tool for multiple applications, including 3D modeling and analysis. Future integration with multimodal data sources, like text, audio, and images, could further expand its applicability to a wide range of real-world scenarios.