Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail
Yiyu Zhuang Qi Zhang Ying Feng Hao Zhu Yao Yao Xiaoyu Li Yan-Pei Cao Ying Shan Xun Cao
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.