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.

我们提出了 LoD-NeuS，一种用于高频几何细节恢复和抗锯齿新视图渲染的高效神经表示。我们借鉴了具有细节级别的基于体素的表示，引入了一种多尺度三平面场景表示方法，能够捕捉有符号距离函数和空间辐射的细节级别。我们的表示通过沿射线在锥体锥内进行多次卷积特征化来聚合空间特征，并通过可微分渲染优化细节级别特征体积。此外，我们提出了一种误差引导的采样策略，在优化过程中引导有符号距离函数的增长。定性和定量评估表明，与最先进的方法相比，我们的方法在表面重建和逼真视图合成方面取得了优越的效果。