Despite the remarkable achievements of neural radiance fields (NeRF) in representing 3D scenes and generating novel view images, the aliasing issue, rendering "jaggies" or "blurry" images at varying camera distances, remains unresolved in most existing approaches. The recently proposed mip-NeRF has addressed this challenge by rendering conical frustums instead of rays. However, it relies on MLP architecture to represent the radiance fields, missing out on the fast training speed offered by the latest grid-based methods. In this work, we present mip-Grid, a novel approach that integrates anti-aliasing techniques into grid-based representations for radiance fields, mitigating the aliasing artifacts while enjoying fast training time. The proposed method generates multi-scale grids by applying simple convolution operations over a shared grid representation and uses the scale-aware coordinate to retrieve features at different scales from the generated multi-scale grids. To test the effectiveness, we integrated the proposed method into the two recent representative grid-based methods, TensoRF and K-Planes. Experimental results demonstrate that mip-Grid greatly improves the rendering performance of both methods and even outperforms mip-NeRF on multi-scale datasets while achieving significantly faster training time. For code and demo videos, please see https://stnamjef.github.io/mipgrid.github.io/.

翻译：尽管神经辐射场（NeRF）在三维场景表示和新视角图像生成方面取得了显著成就，但现有大多数方法仍未解决在不同相机距离下渲染出“锯齿状”或“模糊”图像的抗锯齿问题。最近提出的mip-NeRF通过渲染锥形平截头体而非射线的方式应对了这一挑战，但其依赖MLP架构表示辐射场，错失了基于网格的最新方法所具备的快速训练速度。本文提出mip-Grid，一种将抗锯齿技术集成到基于网格的辐射场表示中的新方法，在缓解锯齿伪影的同时实现快速训练。该方法通过对共享网格表示施加简单卷积运算来生成多尺度网格，并利用尺度感知坐标从生成的多尺度网格中检索不同尺度的特征。为验证有效性，我们将所提方法集成到两种最新代表性网格方法TensoRF和K-Planes中。实验结果表明，mip-Grid显著提升了这两种方法的渲染性能，甚至在多尺度数据集上超越mip-NeRF，同时训练时间大幅缩短。代码和演示视频请见：https://stnamjef.github.io/mipgrid.github.io/。