Squeeze3D：您的三維生成模型實為極致神經壓縮器

Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural Compressor

June 9, 2025

作者: Rishit Dagli, Yushi Guan, Sankeerth Durvasula, Mohammadreza Mofayezi, Nandita Vijaykumar

cs.AI

摘要

我們提出Squeeze3D，這是一種新穎的框架，利用現有預訓練三維生成模型所學到的隱含先驗知識，以極高的壓縮比來壓縮三維數據。我們的方法通過可訓練的映射網絡，將預訓練編碼器與預訓練生成模型之間的潛在空間連接起來。任何以網格、點雲或輻射場表示的三維模型，首先由預訓練編碼器進行編碼，然後轉換（即壓縮）為高度緊湊的潛在代碼。此潛在代碼可有效用作網格或點雲的極度壓縮表示。映射網絡將壓縮後的潛在代碼轉換至強大生成模型的潛在空間，隨後根據此空間條件重建原始三維模型（即解壓縮）。Squeeze3D完全在生成的合成數據上進行訓練，無需任何三維數據集。Squeeze3D架構可靈活地與現有預訓練三維編碼器及生成模型配合使用，並能靈活支持不同格式，包括網格、點雲和輻射場。我們的實驗表明，Squeeze3D在保持視覺質量與多種現有方法相當的同時，對紋理網格實現了高達2187倍的壓縮比，對點雲實現了55倍，對輻射場實現了619倍的壓縮比。由於Squeeze3D不涉及訓練特定於物體的網絡來壓縮物體，因此僅產生極小的壓縮與解壓縮延遲。

English

We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained 3D generative models to compress 3D data at extremely high compression ratios. Our approach bridges the latent spaces between a pre-trained encoder and a pre-trained generation model through trainable mapping networks. Any 3D model represented as a mesh, point cloud, or a radiance field is first encoded by the pre-trained encoder and then transformed (i.e. compressed) into a highly compact latent code. This latent code can effectively be used as an extremely compressed representation of the mesh or point cloud. A mapping network transforms the compressed latent code into the latent space of a powerful generative model, which is then conditioned to recreate the original 3D model (i.e. decompression). Squeeze3D is trained entirely on generated synthetic data and does not require any 3D datasets. The Squeeze3D architecture can be flexibly used with existing pre-trained 3D encoders and existing generative models. It can flexibly support different formats, including meshes, point clouds, and radiance fields. Our experiments demonstrate that Squeeze3D achieves compression ratios of up to 2187x for textured meshes, 55x for point clouds, and 619x for radiance fields while maintaining visual quality comparable to many existing methods. Squeeze3D only incurs a small compression and decompression latency since it does not involve training object-specific networks to compress an object.