MonSter++：基于单目深度先验的统一立体匹配、多视点立体与实时立体方法
MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors
AI总结：
MonSter++通过双分支架构融合单目深度先验与多视点线索，迭代互增强解决病态区域匹配难题，在立体匹配、实时立体和多视点立体八个基准上达到最先进水平。
摘要：
我们提出了MonSter++，一个用于多视点深度估计的几何基础模型，统一了校正立体匹配和非校正多视点立体。这两类任务从根本上都是通过对应搜索恢复度量深度，因此面临同样的困境：难以处理匹配线索有限的病态区域。为解决这一问题，我们提出了MonSter++，一种将单目深度先验集成到多视点深度估计中的新方法，有效结合了单视点与多视点线索的互补优势。MonSter++将单目深度和多视点深度融合到一个双分支架构中。基于置信度的引导自适应地选择可靠的多视点线索来校正单目深度中的尺度模糊性。反过来，精炼后的单目预测在病态区域有效引导多视点估计。这种迭代的相互增强使MonSter++能够将粗糙的物体级单目先验演化为细粒度的像素级几何，充分释放多视点深度估计的潜力。MonSter++在立体匹配和多视点立体两个任务上都达到了新的最先进水平。通过我们级联搜索和多尺度深度融合策略有效整合单目先验，我们的实时变体RT-MonSter++也大幅超越了之前的实时方法。如图1所示，MonSter++在来自三个任务（立体匹配、实时立体匹配和多视点立体）的八个基准上相比之前方法取得了显著改进，展示了我们框架的强大泛化能力。除了高精度外，MonSter++还展现了优越的零样本泛化能力。我们将发布大型模型和实时模型，以方便开源社区使用。
英文摘要：
We introduce MonSter++, a geometric foundation model for multi-view depth estimation, unifying rectified stereo matching and unrectified multi-view stereo. Both tasks fundamentally recover metric depth from correspondence search and consequently face the same dilemma: struggling to handle ill-posed regions with limited matching cues. To address this, we propose MonSter++, a novel method that integrates monocular depth priors into multi-view depth estimation, effectively combining the complementary strengths of single-view and multi-view cues. MonSter++ fuses monocular depth and multi-view depth into a dual-branched architecture. Confidence-based guidance adaptively selects reliable multi-view cues to correct scale ambiguity in monocular depth. The refined monocular predictions, in turn, effectively guide multi-view estimation in ill-posed regions. This iterative mutual enhancement enables MonSter++ to evolve coarse object-level monocular priors into fine-grained, pixel-level geometry, fully unlocking the potential of multi-view depth estimation. MonSter++ achieves new state-of-the-art on both stereo matching and multi-view stereo. By effectively incorporating monocular priors through our cascaded search and multi-scale depth fusion strategy, our real-time variant RT-MonSter++ also outperforms previous real-time methods by a large margin. As shown in Fig.1, MonSter++ achieves significant improvements over previous methods across eight benchmarks from three tasks -- stereo matching, real-time stereo matching, and multi-view stereo, demonstrating the strong generality of our framework. Besides high accuracy, MonSter++ also demonstrates superior zero-shot generalization capability. We will release both the large and the real-time models to facilitate their use by the open-source community.
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