Recent studies have witnessed that self-supervised methods based on view
synthesis obtain clear progress on multi-view stereo (MVS). However, existing
methods rely on the assumption that the corresponding points among different
views share the same color, which may not always be true in practice. This may
lead to unreliable self-supervised signal and harm the final reconstruction
performance. To address the issue, we propose a framework integrated with more
reliable supervision guided by semantic co-segmentation and data-augmentation.
Specially, we excavate mutual semantic from multi-view images to guide the
semantic consistency. And we devise effective data-augmentation mechanism which
ensures the transformation robustness by treating the prediction of regular
samples as pseudo ground truth to regularize the prediction of augmented
samples. Experimental results on DTU dataset show that our proposed methods
achieve the state-of-the-art performance among unsupervised methods, and even
compete on par with supervised methods. Furthermore, extensive experiments on
Tanks&Temples dataset demonstrate the effective generalization ability of the
proposed method.

针对现有自监督方法在多视图重建中可能出现的相关点之间颜色不同的问题，本文提出了一种基于语义共分割和数据增强的更可靠监督的框架，其中利用多视图图像中的相互语义来指导语义一致性，同时设计了有效的数据增强机制，以确保对样本的变换鲁棒性。在 DTU 数据集上的实验结果表明，我们提出的方法在无监督方法中取得了最先进的性能，并且甚至可以与有监督方法一较高下。此外，在 Tanks＆Temples 数据集上的广泛实验证明了该方法的有效泛化能力。

通过有效的协同分割和数据增强实现自监督的多视图立体匹配

Self-supervised Multi-view Stereo via Effective Co-Segmentation and  Data-Augmentation

This paper proposes key instance selection based on video saliency covering
objectness and dynamics for unsupervised video object segmentation (UVOS). Our
method takes frames sequentially and extracts object proposals with
corresponding masks for each frame. We link objects according to their
similarity until the M-th frame and then assign them unique IDs (i.e.,
instances). Similarity measure takes into account multiple properties such as
ReID descriptor, expected trajectory, and semantic co-segmentation result.
After M-th frame, we select K IDs based on video saliency and frequency of
appearance; then only these key IDs are tracked through the remaining frames.
Thanks to these technical contributions, our results are ranked third on the
leaderboard of UVOS DAVIS challenge.

本文提出了一种基于视频显着性的关键实例选择方法，涵盖了对象性和动态特性，用于非监督式视频对象分割，在多个属性如 ReID 描述符，预期轨迹和语义共分割结果等方面考虑相似性度量，并基于视频显着性和出现频率选择关键 ID 进行跟踪。这些技术贡献使得我们在 UVOS DAVIS 挑战的排行榜上排名第三。