We present NeCGS, the first neural compression paradigm, which can compress a geometry set encompassing thousands of detailed and diverse 3D mesh models by up to 900 times with high accuracy and preservation of detailed geometric structures. Specifically, we first propose TSDF-Def, a new implicit representation that is capable of \textbf{accurately} representing irregular 3D mesh models with various structures into regular 4D tensors of \textbf{uniform} and \textbf{compact} size, where 3D surfaces can be extracted through the deformable marching cubes. Then we construct a quantization-aware auto-decoder network architecture to regress these 4D tensors to explore the local geometric similarity within each shape and across different shapes for redundancy removal, resulting in more compact representations, including an embedded feature of a smaller size associated with each 3D model and a network parameter shared by all models. We finally encode the resulting features and network parameters into bitstreams through entropy coding. Besides, our NeCGS can handle the dynamic scenario well, where new 3D models are constantly added to a compressed set. Extensive experiments and ablation studies demonstrate the significant advantages of our NeCGS over state-of-the-art methods both quantitatively and qualitatively. The source code is available at https://github.com/rsy6318/NeCGS.

翻译：本文提出了NeCGS，首个神经压缩范式，能够以高达900倍的压缩比对包含数千个精细多样三维网格模型的几何集合进行高精度压缩，同时保持详细几何结构。具体而言，我们首先提出TSDF-Def——一种新颖的隐式表示方法，能够将具有多样结构的不规则三维网格模型**精确**表示为尺寸**统一**且**紧凑**的规则四维张量，并通过可变形移动立方体算法从中提取三维表面。随后，我们构建了量化感知的自解码器网络架构，通过回归这些四维张量来挖掘单个形状内部及不同形状间的局部几何相似性以消除冗余，从而生成更紧凑的表示形式，包括与每个三维模型关联的较小尺寸嵌入特征，以及所有模型共享的网络参数。最后，我们通过熵编码将生成的特征和网络参数编码为比特流。此外，我们的NeCGS能够很好地处理动态场景，即不断向已压缩集合中添加新三维模型的情况。大量实验与消融研究证明，NeCGS在定量与定性评估上均显著优于现有先进方法。源代码发布于https://github.com/rsy6318/NeCGS。