用主动学习减少语义神经辐射场标注成本，效果超随机采样两倍。
Exploring Active Learning for Label-Efficient Training of Semantic Neural Radiance Field
- 基于3D几何约束设计新采样策略，提升标注效率。
- 相比随机采样，标注成本降低2倍以上。
- 适合需要高效标注的三维场景理解研究者。
神经辐射场（NeRF）是隐式神经场景表示方法，在新视角生成上表现卓越。语义感知的NeRF不仅捕捉场景形状与辐射信息，还编码语义内容。训练此类模型通常需像素级类别标签，标注成本极高。本文探索主动学习作为缓解标注负担的方案，研究了样本选择粒度与策略等设计。提出一种融合3D几何约束的新主动学习策略。实验表明，该方法可显著降低标注成本，相比随机采样实现超过2倍的节省。
原文摘要 · Abstract (English)
Neural Radiance Field (NeRF) models are implicit neural scene representation methods that offer unprecedented capabilities in novel view synthesis. Semantically-aware NeRFs not only capture the shape and radiance of a scene, but also encode semantic information of the scene. The training of semantically-aware NeRFs typically requires pixel-level class labels, which can be prohibitively expensive to collect. In this work, we explore active learning as a potential solution to alleviate the annotation burden. We investigate various design choices for active learning of semantically-aware NeRF, including selection granularity and selection strategies. We further propose a novel active learning strategy that takes into account 3D geometric constraints in sample selection. Our experiments demonstrate that active learning can effectively reduce the annotation cost of training semantically-aware NeRF, achieving more than 2X reduction in annotation cost compared to random sampling.
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