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.

本研究针对现有隐式神经表示模型仅支持单一细节层次的局限，提出了一种新的形状建模方法，能够实现多层次细节且确保每层的光滑表面。通过引入一种新颖的潜在调节机制，在多尺度和带宽限制的神经架构下，显著提高了隐式场景渲染的效率，其精度在细节层次上可媲美当前最佳的单细节模型。

多细节层次的潜在隐式三维形状模型