CVAIDec 10, 2021

BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering

arXiv:2112.05504v4315 citations
Originality Incremental advance
AI Analysis

This addresses a real-world challenge in 3D scene modeling for applications like city planning and virtual environments, though it is an incremental improvement over existing NeRF methods.

The paper tackled the problem of rendering multi-scale 3D scenes with drastically varying views, such as from satellite to ground level, by introducing BungeeNeRF, a progressive neural radiance field that achieves high-quality level-of-detail rendering across scales.

Neural radiance fields (NeRF) has achieved outstanding performance in modeling 3D objects and controlled scenes, usually under a single scale. In this work, we focus on multi-scale cases where large changes in imagery are observed at drastically different scales. This scenario vastly exists in real-world 3D environments, such as city scenes, with views ranging from satellite level that captures the overview of a city, to ground level imagery showing complex details of an architecture; and can also be commonly identified in landscape and delicate minecraft 3D models. The wide span of viewing positions within these scenes yields multi-scale renderings with very different levels of detail, which poses great challenges to neural radiance field and biases it towards compromised results. To address these issues, we introduce BungeeNeRF, a progressive neural radiance field that achieves level-of-detail rendering across drastically varied scales. Starting from fitting distant views with a shallow base block, as training progresses, new blocks are appended to accommodate the emerging details in the increasingly closer views. The strategy progressively activates high-frequency channels in NeRF's positional encoding inputs and successively unfolds more complex details as the training proceeds. We demonstrate the superiority of BungeeNeRF in modeling diverse multi-scale scenes with drastically varying views on multiple data sources (city models, synthetic, and drone captured data) and its support for high-quality rendering in different levels of detail.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes