提出多细节层级的隐式3D形状模型，实现快速平滑重建。
A Latent Implicit 3D Shape Model for Multiple Levels of Detail
- 用多尺度带宽受限网络与新潜变量条件，实现多层级细节建模。
- 早期层快速输出近似SDF，细粒度重建质量达顶尖单级模型水平。
- 适合需要高效渲染和多分辨率输出的3D生成与场景重建任务。
隐式神经表示将形状特定的潜码与3D坐标映射为对应的有符号距离（SDF）值。然而，该方法仅提供单一细节层级。浅层网络可模拟低细节，但生成形状通常不光滑；部分网络设计虽支持多层级细节，却仅限于过拟合单个对象。为此，我们提出一种新形状建模方法，可在多个层级上保持平滑表面，并保证各层级细节一致性。核心在于引入一种新型潜变量条件，用于多尺度且带宽受限的神经架构。该架构实现了对多个形状的深层参数化，早期层能快速输出近似SDF值，从而在单个网络中平衡速度与精度，提升隐式场景渲染效率。通过限制网络带宽，可在所有细节层级维持平滑表面。在更精细层级上，重建质量达到当前仅支持单层级的先进模型水平。
原文摘要 · Abstract (English)
Implicit neural representations map a shape-specific latent code and a 3D coordinate to its corresponding signed distance (SDF) value. However, this approach only offers a single level of detail. Emulating low levels of detail can be achieved with shallow networks, but the generated shapes are typically not smooth. Alternatively, some network designs offer multiple levels of detail, but are limited to overfitting a single object. To address this, we propose a new shape modeling approach, which enables multiple levels of detail and guarantees a smooth surface at each level. At the core, we introduce a novel latent conditioning for a multiscale and bandwith-limited neural architecture. This results in a deep parameterization of multiple shapes, where early layers quickly output approximated SDF values. This allows to balance speed and accuracy within a single network and enhance the efficiency of implicit scene rendering. We demonstrate that by limiting the bandwidth of the network, we can maintain smooth surfaces across all levels of detail. At finer levels, reconstruction quality is on par with the state of the art models, which are limited to a single level of detail.
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