Despite the impressive performance of Multi-view Stereo (MVS) approaches
given plenty of training samples, the performance degradation when generalizing
to unseen domains has not been clearly explored yet. In this work, we focus on
the domain generalization problem in MVS. To evaluate the generalization
results, we build a novel MVS domain generalization benchmark including
synthetic and real-world datasets. In contrast to conventional domain
generalization benchmarks, we consider a more realistic but challenging
scenario, where only one source domain is available for training. The MVS
problem can be analogized back to the feature matching task, and maintaining
robust feature consistency among views is an important factor for improving
generalization performance. To address the domain generalization problem in
MVS, we propose a novel MVS framework, namely RobustMVS. A
DepthClustering-guided Whitening (DCW) loss is further introduced to preserve
the feature consistency among different views, which decorrelates multi-view
features from viewpoint-specific style information based on geometric priors
from depth maps. The experimental results further show that our method achieves
superior performance on the domain generalization benchmark.

本研究针对多视点立体重建（Multi-view Stereo，MVS）领域中的领域泛化问题，提出了一种名为 RobustMVS 的新型框架，并引入 DepthClustering-guided Whitening（DCW）损失以维持不同视角之间的特征一致性，实验结果表明我们的方法在领域泛化基准测试中取得了优越性能。

RobustMVS: 单域通用深度多视角立体视觉

RobustMVS: Single Domain Generalized Deep Multi-view Stereo

Recent researches attempt to improve the detection performance by adopting
the idea of cascade for single-stage detectors. In this paper, we analyze and
discover that inconsistency is the major factor limiting the performance. The
refined anchors are associated with the feature extracted from the previous
location and the classifier is confused by misaligned classification and
localization. Further, we point out two main designing rules for the cascade
manner: improving consistency between classification confidence and
localization performance, and maintaining feature consistency between different
stages. A multistage object detector named Cas-RetinaNet, is then proposed for
reducing the misalignments. It consists of sequential stages trained with
increasing IoU thresholds for improving the correlation, and a novel Feature
Consistency Module for mitigating the feature inconsistency. Experiments show
that our proposed Cas-RetinaNet achieves stable performance gains across
different models and input scales. Specifically, our method improves RetinaNet
from 39.1 AP to 41.1 AP on the challenging MS COCO dataset without any bells or
whistles.

本文提出了一种名为 Cas-RetinaNet 的级联目标检测器来降低不一致性和提高性能。该算法包括按照递增的 IoU 阈值进行训练的多个级别和一个用于减轻特征不一致性的新特征一致性模块。实验表明我们的算法相对于 RetinaNet，在不采用其他技巧的情况下，将在具有挑战性的 MS COCO 数据集上的表现从 39.1 提升至 41.1。