Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs

TL;DR

Mega-NeRF enables scalable, high-quality, and fast rendering of large-scale 3D scenes from drone-captured data by employing specialized network structures and data parallelism, significantly improving training and rendering efficiency.

Contribution

The paper introduces a sparse, region-specialized neural radiance field architecture and a geometric clustering algorithm for data parallelism, addressing large-scale scene modeling challenges.

Findings

Training speed improved by 3x

PSNR increased by 12%

Achieves 40x faster rendering with minimal quality loss

Abstract

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale visual captures spanning buildings or even multiple city blocks collected primarily from drones. In contrast to single object scenes (on which NeRFs are traditionally evaluated), our scale poses multiple challenges including (1) the need to model thousands of images with varying lighting conditions, each of which capture only a small subset of the scene, (2) prohibitively large model capacities that make it infeasible to train on a single GPU, and (3) significant challenges for fast rendering that would enable interactive fly-throughs. To address these challenges, we begin by analyzing visibility statistics for large-scale scenes, motivating a sparse network structure where parameters are specialized to different regions of the scene. We introduce a simple geometric clustering algorithm for data parallelism that partitions training images (or rather pixels) into different NeRF submodules that can be trained in parallel. We evaluate our approach on existing datasets (Quad 6k and UrbanScene3D) as well as against our own drone footage, improving training speed by 3x and PSNR by 12%. We also evaluate recent NeRF fast renderers on top of Mega-NeRF and introduce a novel method that exploits temporal coherence. Our technique achieves a 40x speedup over conventional NeRF rendering while remaining within 0.8 db in PSNR quality, exceeding the fidelity of existing fast renderers.