High-Fidelity and Topology-Lossless Lattice Compression via Learned Context Models

Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties and material efficiency. However, a lattice structure with a huge number of struts would consume massive memory and storage space, which hinders its practical deployment in large-scale design and manufacturing scenarios. The storage challenge becomes even more pressing when moving from individual lattices to assembled lattice models. To solve this issue, we propose the Context-based Lattice Compression (CLC) framework, which leverages learned context models to provide a storage-friendly lattice representation. Specifically, we construct two sets of multi-resolution learnable hash embeddings that separately encode the lattice nodes and struts, and then excavate context dependencies in the embeddings to enable probability prediction for information entropy reduction. Additionally, we exploit a context model to accurately estimate the probability of geometric residuals, which are crucial for high-fidelity restoration, and we also use Delta Encoding to compress the correction edge set for lossless topology reconstruction. We also utilize feature pruning and hash fusion to further improve compactness and context quality across levels. This design allows the framework to jointly address storage reduction, geometry restoration, and topology preservation within a unified compression pipeline. Quantitative and qualitative evaluations demonstrate that CLC achieves up to 16.58$\times$ compression over vanilla lattice representation, achieving a 1.95$\times$ size reduction over the SOTA lattice compression approach while simultaneously improving fidelity and topological integrity.