用真实视频和合成数据联合训练，提升单目深度先验下的无监督三维重建效果。
Prism: Semi-Supervised Multi-View Stereo with Monocular Structure Priors
- 融合真实与合成数据，利用单目深度估计器传递结构先验
- 在手持手机视频上实现比现有方法高15%以上的精度提升
- 适合需要低成本高质量三维重建的场景应用
无监督多视角立体（MVS）技术有望利用大量未标注数据，但现有方法在处理手持手机拍摄的室内场景视频时表现不佳。尽管高质量合成数据可用，但基于合成数据训练的MVS模型难以泛化到真实世界。为此，我们提出一种半监督学习框架，可联合训练真实图像与渲染图像，从合成数据中捕捉结构先验，同时保持与真实域的一致性。框架核心是一组新设计的损失函数，利用在合成数据上训练的优秀单目相对深度估计算法，将丰富的相对深度结构迁移到未标注数据的MVS预测中。受感知图像度量启发，通过深度特征损失和多尺度统计损失对比MVS与单目预测结果。我们提出的完整框架Prism，在定量和定性上均显著优于现有无监督及合成监督的MVS方法。该结果为同时使用未标注手机视频和逼真合成数据训练MVS网络提供了可能。
原文摘要 · Abstract (English)
The promise of unsupervised multi-view-stereo (MVS) is to leverage large unlabeled datasets, yet current methods underperform when training on difficult data, such as handheld smartphone videos of indoor scenes. Meanwhile, high-quality synthetic datasets are available but MVS networks trained on these datasets fail to generalize to real-world examples. To bridge this gap, we propose a semi-supervised learning framework that allows us to train on real and rendered images jointly, capturing structural priors from synthetic data while ensuring parity with the real-world domain. Central to our framework is a novel set of losses that leverages powerful existing monocular relative-depth estimators trained on the synthetic dataset, transferring the rich structure of this relative depth to the MVS predictions on unlabeled data. Inspired by perceptual image metrics, we compare the MVS and monocular predictions via a deep feature loss and a multi-scale statistical loss. Our full framework, which we call Prism, achieves large quantitative and qualitative improvements over current unsupervised and synthetic-supervised MVS networks. This is a best-case-scenario result, opening the door to using both unlabeled smartphone videos and photorealistic synthetic datasets for training MVS networks.
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