The recent proliferation of 3D content that can be consumed on hand-held devices necessitates efficient tools for transmitting large geometric data, e.g., 3D meshes, over the Internet. Detailed high-resolution assets can pose a challenge to storage as well as transmission bandwidth, and level-of-detail techniques are often used to transmit an asset using an appropriate bandwidth budget. It is especially desirable for these methods to transmit data progressively, improving the quality of the geometry with more data. Our key insight is that the geometric details of 3D meshes often exhibit similar local patterns even across different shapes, and thus can be effectively represented with a shared learned generative space. We learn this space using a subdivision-based encoder-decoder architecture trained in advance on a large collection of surfaces. We further observe that additional residual features can be transmitted progressively between intermediate levels of subdivision that enable the client to control the tradeoff between bandwidth cost and quality of reconstruction, providing a neural progressive mesh representation. We evaluate our method on a diverse set of complex 3D shapes and demonstrate that it outperforms baselines in terms of compression ratio and reconstruction quality.

翻译：近年来，可在手持设备上消费的三维内容激增，这亟需高效的互联网大型几何数据（如三维网格）传输工具。高分辨率精细资产对存储和传输带宽构成挑战，多级细节技术常被用于在适当带宽预算下传输资产。这些方法尤其需要具备渐进传输能力，即通过增加数据量逐步提升几何质量。我们的关键洞察在于：三维网格的几何细节即使在不同形状间也常呈现相似的局部模式，因此可通过共享的学习生成空间高效表达。我们利用基于细分编码器-解码器架构来学习该空间，该架构预先在大量曲面集合上训练。进一步观察到，在细分层级之间可渐进传输额外残差特征，使客户端能够控制带宽成本与重建质量之间的权衡，从而形成神经渐进网格表示。我们在多样化的复杂三维形状上评估该方法，结果表明其在压缩比和重建质量方面均优于基线方法。