Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detail
This work addresses the challenge of efficient neural scene representation for 3D reconstruction and rendering, offering improvements in detail recovery and anti-aliasing for applications in computer vision and graphics.
The paper tackles the problem of recovering high-frequency geometry detail and achieving anti-aliased novel view rendering by introducing LoD-NeuS, a multi-scale tri-plane-based neural representation that captures level of detail for signed distance functions and radiance, resulting in superior surface reconstruction and photorealistic view synthesis compared to state-of-the-art methods.
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.