The rendering scheme in neural radiance field (NeRF) is effective in
rendering a pixel by casting a ray into the scene. However, NeRF yields blurred
rendering results when the training images are captured at non-uniform scales,
and produces aliasing artifacts if the test images are taken in distant views.
To address this issue, Mip-NeRF proposes a multiscale representation as a
conical frustum to encode scale information. Nevertheless, this approach is
only suitable for offline rendering since it relies on integrated positional
encoding (IPE) to query a multilayer perceptron (MLP). To overcome this
limitation, we propose mip voxel grids (Mip-VoG), an explicit multiscale
representation with a deferred architecture for real-time anti-aliasing
rendering. Our approach includes a density Mip-VoG for scene geometry and a
feature Mip-VoG with a small MLP for view-dependent color. Mip-VoG encodes
scene scale using the level of detail (LOD) derived from ray differentials and
uses quadrilinear interpolation to map a queried 3D location to its features
and density from two neighboring downsampled voxel grids. To our knowledge, our
approach is the first to offer multiscale training and real-time anti-aliasing
rendering simultaneously. We conducted experiments on multiscale datasets, and
the results show that our approach outperforms state-of-the-art real-time
rendering baselines.

本文介绍了一种名为 Mip-VoG 的新的多尺度渲染技术，采用四线性插值将 3D 场景中的位置映射到其特征和密度。相比传统的 NeRF 和 Mip-NeRF 方法，Mip-VoG 在同时提高多尺度训练和实时抗锯齿渲染方面具有更好的性能。