Abstract
Optimizing the memory footprint of 3D models can have a major impact on the user experiences during real-time rendering and streaming visualization, where the major memory overhead lies in high-resolution texture data. In this work, we propose a robust and automatic pipeline to content-aware, lossy compression for texture atlas. The design of our solution lies in two observations- 1) mapping multiple surface patches to the same texture region is seamlessly compatible with the standard rendering pipeline, requiring no decompression before any usage; 2) a texture image has background regions and salient structural features, which can be handled separately to achieve a high compression rate. Accordingly, our method contains three phases. We first factor out redundant salient texture contents by detecting such regions and mapping their corresponding 3D surface patches to a single UV patch via a UV-preserving re-meshing procedure. We then compress redundant background content by clustering triangles into groups by their color. Finally, we create a new UV atlas with all repetitive texture contents removed and bake a new texture via differentiable rendering to remove potential inter-patch artifacts. To evaluate the efficacy of our approach, we batch-processed a dataset containing 100 models collected online. On average, our method achieves a texture atlas compression ratio of 81.80% with an averaged PSNR and MS-SSIM scores of 40.68 and 0.97, a marginal error in visual appearance.