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.

翻译：神经辐射场（NeRF）的渲染方案通过向场景投射光线来渲染像素，效果显著。然而，当训练图像以非均匀尺度捕获时，NeRF会产生模糊的渲染结果，若测试图像在远距离视角下拍摄，则会出现锯齿伪影。为解决此问题，Mip-NeRF提出了一种多尺度表示方法，利用锥形截锥体编码尺度信息。但该方法依赖于集成位置编码（IPE）查询多层感知机（MLP），仅适用于离线渲染。为克服这一局限，我们提出了mip体素网格（Mip-VoG），一种采用延迟架构的显式多尺度表示，用于实时抗锯齿渲染。该方法包括用于场景几何的密度Mip-VoG，以及用于视角相关颜色、附带小型MLP的特征Mip-VoG。Mip-VoG利用光线微分导出的细节级别（LOD）编码场景尺度，并通过四线性插值将查询的三维位置映射到来自两个相邻下采样体素网格的特征与密度。据我们所知，本方法首次同时实现了多尺度训练与实时抗锯齿渲染。在多尺度数据集上的实验结果表明，我们的方法优于最先进的实时渲染基线方法。