We present LoD-NeuS, an efficient neural representation for high-frequency geometry detail recovery and anti-aliased novel view rendering. Drawing inspiration from voxel-based representations with the level of detail (LoD), we introduce a multi-scale tri-plane-based scene representation that is capable of capturing the LoD of the signed distance function (SDF) and the space radiance. Our representation aggregates space features from a multi-convolved featurization within a conical frustum along a ray and optimizes the LoD feature volume through differentiable rendering. Additionally, we propose an error-guided sampling strategy to guide the growth of the SDF during the optimization. Both qualitative and quantitative evaluations demonstrate that our method achieves superior surface reconstruction and photorealistic view synthesis compared to state-of-the-art approaches.

翻译：我们提出LoD-NeuS，一种用于高频几何细节恢复与抗锯齿新视角渲染的高效神经表示方法。受具有细节层级（LoD）的体素表示启发，我们引入了一种基于多尺度三平面场景表示，能够捕获符号距离函数（SDF）与空间辐射场的LoD。该表示通过沿射线的锥形截锥体内的多卷积特征化聚合空间特征，并通过可微渲染优化LoD特征体。此外，我们提出了一种误差引导采样策略，以引导优化过程中SDF的生长。定性与定量评估均表明，与现有最优方法相比，本方法在曲面重建与逼真视图合成方面取得了更优效果。