用多源辅助任务提升单目深度估计，数据量减80%仍更准
Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks
- 共享解码器+交替训练，用预训练模型融合多任务数据
- 平均深度估计精度提升11%，小数据集效果翻倍
- 语义分割类数据尤其有效，适合资源有限的研究者
单目深度估计（MDE）在计算机视觉中极具挑战，常受高质量标注数据稀缺和成本高的制约。本文提出一种交替训练策略，利用相关视觉任务的辅助数据，在基于预训练视觉基础模型的共享解码器上进行训练，并对MDE任务赋予更高权重。大量实验表明，引入多种域内辅助数据与任务可使MDE性能平均提升约11%。分析显示，不同辅助任务影响各异，强调任务选择的重要性——单纯堆加数据无法带来增益。特别地，将语义分割数据作为多标签密集分类（MLDC）使用时，常能带来额外性能提升。此外，该方法显著提高数据效率，可在至少减少80%数据量的前提下，仍保持甚至提升现有MDE数据集的质量。这为在高质标注数据匮乏场景下，通过跨任务辅助数据提升深度估计性能提供了可行路径。代码已公开于 https://jugit.fz-juelich.de/ias-8/mdeaux。
原文摘要 · Abstract (English)
Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using auxiliary datasets from related vision tasks for an alternating training scheme with a shared decoder built on top of a pre-trained vision foundation model, while giving a higher weight to MDE. Through extensive experiments we demonstrate the benefits of incorporating various in-domain auxiliary datasets and tasks to improve MDE quality on average by ~11%. Our experimental analysis shows that auxiliary tasks have different impacts, confirming the importance of task selection, highlighting that quality gains are not achieved by merely adding data. Remarkably, our study reveals that using semantic segmentation datasets as Multi-Label Dense Classification (MLDC) often results in additional quality gains. Lastly, our method significantly improves the data efficiency for the considered MDE datasets, enhancing their quality while reducing their size by at least 80%. This paves the way for using auxiliary data from related tasks to improve MDE quality despite limited availability of high-quality labeled data. Code is available at https://jugit.fz-juelich.de/ias-8/mdeaux.
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