Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields

TL;DR

Mip-Grid introduces an anti-aliased grid-based representation for neural radiance fields, significantly reducing artifacts and improving rendering quality while maintaining fast training speeds, outperforming previous methods like mip-NeRF.

Contribution

The paper presents mip-Grid, a novel grid-based approach that incorporates anti-aliasing techniques into radiance field representations, enhancing quality and speed over existing methods.

Findings

Mip-Grid reduces aliasing artifacts in NeRF rendering.

It outperforms mip-NeRF on multi-scale datasets.

The method achieves faster training times than mip-NeRF.

Abstract

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/.