通过不确定性采样增强低数据场景下的NeRF训练，提升重建效果与稳定性。
Data Augmentation for NeRFs in the Low Data Limit
- 基于体积不确定性与空间覆盖生成后验分布，拒绝采样新增视角。
- 相比顶尖基线，重建平均提升39.9%，方差降低87.5%。
- 适用于机器人等资源受限、未知环境下的数据增广任务。
当前基于神经辐射场（NeRF）的方法在低数据条件下表现不佳，尤其在不完整场景数据上。已有工作仅在最优视角选择中进行数据增强，导致稀疏数据下出现幻觉与模型崩溃。本文提出在训练过程中通过结合体素不确定性估计与空间覆盖率生成的后验不确定性分布，采用拒绝采样方式添加新视角。我们在部分观测场景上验证了该方法，平均性能优于现有最佳基线39.9%，且跨基准测试的方差减少87.5%。进一步表明，从任意分布采样扩充训练集均能提升稀疏环境下的重建质量与一致性。本工作为资源受限、先验未知环境中的机器人任务提供了基础支持。视频与源码见https://murpheylab.github.io/low-data-nerf/。
原文摘要 · Abstract (English)
Current methods based on Neural Radiance Fields fail in the low data limit, particularly when training on incomplete scene data. Prior works augment training data only in next-best-view applications, which lead to hallucinations and model collapse with sparse data. In contrast, we propose adding a set of views during training by rejection sampling from a posterior uncertainty distribution, generated by combining a volumetric uncertainty estimator with spatial coverage. We validate our results on partially observed scenes; on average, our method performs 39.9% better with 87.5% less variability across established scene reconstruction benchmarks, as compared to state of the art baselines. We further demonstrate that augmenting the training set by sampling from any distribution leads to better, more consistent scene reconstruction in sparse environments. This work is foundational for robotic tasks where augmenting a dataset with informative data is critical in resource-constrained, a priori unknown environments. Videos and source code are available at https://murpheylab.github.io/low-data-nerf/.
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