最新的多视图深度估计方法是在深度视频或多视图立体设置中采用的。尽管设置不同，但这些方法在技术上是相似的：它们将多个源视图与关键视图相关联，以估算关键视图的深度图。在这项工作中，我们介绍了强大的多视图深度基准，该基准构建在一组公共数据集上，并允许在两个设置中对来自不同域的数据进行评估。我们评估了最近的方法，并发现跨领域的性能不平衡。此外，我们考虑了第三个设置，可以使用相机姿势，目的是用正确的尺度估算相应的深度图。我们表明，最近的方法不会在这种情况下跨数据集概括。这是因为它们的成本量输出不足。为了解决这一问题，我们介绍了多视图深度估计的强大MVD基线模型，该模型构建在现有组件上，但采用了新颖的规模增强程序。它可以应用于与目标数据无关的强大多视图深度估计。我们在https://github.com/lmb-freiburg/robustmvd上为建议的基准模型提供了代码。

Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g., a pre-trained VGG, to obtain a latent representation. However, due to domain shift, pre-trained models from the natural image domain might not apply to other image domains, such as medical imaging. Notably, in medical imaging, evaluating the perceptual similarity is exclusively performed by specialists trained extensively in diverse medical fields. Thus, medical imaging remains devoid of task-specific, objective perceptual measures. This work answers the question: Is it necessary to rely on supervised learning to obtain an effective representation that could measure perceptual similarity, or is self-supervision sufficient? To understand whether recent contrastive self-supervised representation (CSR) may come to the rescue, we start with natural images and systematically evaluate CSR as a metric across numerous contemporary architectures and tasks and compare them with existing methods. We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings. We also demonstrate that CSR can significantly improve image quality in two image synthesis tasks. Finally, our extensive results suggest that perceptuality is an emergent property of CSR, which can be adapted to many image domains without requiring annotations.

统计形状建模旨在捕获给定种群中发生的解剖结构的形状变化。形状模型用于许多任务，例如形状重建和图像分割，但也可以塑造生成和分类。现有的形状先验需要训练示例之间的密集对应，或者缺乏鲁棒性和拓扑保证。我们提出了FlowSM，这是一种新型的形状建模方法，它可以学习形状变异性，而无需在训练实例之间密集的对应关系。它依赖于连续变形流的层次结构，该层次由神经网络参数化。我们的模型优于远端股骨和肝脏在提供表现力和稳健形状方面的最先进方法。我们表明，新兴的潜在表示通过将健康与病理形状分开来歧视。最终，我们从部分数据中证明了其对两个形状重建任务的有效性。我们的源代码公开可用（https://github.com/davecasp/flowssm）。