Texture Atlas Compression Based on Repeated Content Removal

 

Proceedings of Siggraph Asia'2023, Sydney, Australia, 12-15 December, 2023.

Yuzhe Luo, Xiaogang Jin, Zherong Pan, Kui Wu, Qilong Kou, Xiajun Yang, Xifeng Gao

 

A corner of a bedroom full of complex 3D models with the original texture shown in (a). We use our method to compress the texture of each model and then arrange them together (b). We use three zoom-in views to highlight the marginal visual modification due to our method. Compared with the input, we achieve a texture compression ratio of 83.50% while preserving the visual appearance with the score of 43.19/0.987 as measured by PSNR/MS-SSIM.

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 the 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.

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