TCG-AR：面向集换式卡牌游戏直播的实时多视角增强现实
TCG-AR: Real-Time Multi-View Augmented Reality for Trading Card Game Streaming
AI总结：
提出TCG-AR实时流水线，仅用普通RGB摄像头实现无标记卡牌检测、定向与识别，自动生成合成训练数据，并输出增强直播流。
摘要：
集换式卡牌游戏越来越多地在线上进行和直播，然而直播画面大多局限于游戏区域的平面俯视图。用虚拟模型增强此类直播中的卡牌可以改善观看体验，但现有系统大多依赖带传感器的游戏垫和嵌入式芯片，对休闲玩家和大型活动而言成本高昂且不实用。在这项工作中，我们提出了TCG-AR，一种新颖的实时流水线，仅使用普通RGB摄像头，无需任何物理标记或专用硬件即可增强集换式卡牌游戏。我们的流水线检测、定向并识别桌面上的卡牌，在所有视角下为每张卡牌渲染虚拟内容，还可以合成一种广播视角来总结游戏状态供观众观看，并将增强后的视频流输出到标准直播软件（如OBS）。为了训练检测、定向和识别模型而无需人工标注，我们引入了一种自动流程，从参考卡牌图像集生成带注释的合成训练数据。然后，我们在一个包含真实图像的新手动标注数据集上评估了多个训练模型，分析了决定实际可用性的性能和运行时吞吐量。总体而言，通过仅依赖普通摄像头和硬件，并开源所有代码、模型和数据集，这项工作旨在为实时集换式卡牌识别提供参考，并使实时增强现实直播对更广泛的玩家和主播社区可用。
英文摘要：
Trading card games are increasingly played and broadcast online, yet live streams remain mostly limited to flat top-down footage of the playing area. Augmenting such streams with virtual models of the played cards would improve the viewing experience, but most existing systems rely on instrumented playing surfaces and embedded chips, which are costly and impractical for casual players and large-scale events. In this work, we present TCG-AR, a novel real-time pipeline that augments trading card games using ordinary RGB cameras alone, without any physical markers or specialized hardware. Our pipeline detects, orients, and identifies the cards on the board, renders virtual content onto each card across all views, and can additionally compose a broadcaststyle view that summarizes the game state for spectators, streaming the augmented feeds to standard broadcasting software such as OBS. To train the detection, orientation, and identification models without manual labeling, we introduce an automatic procedure that generates annotated synthetic training data from a reference set of card images. Then, we evaluate several trained models on a new manually annotated dataset with real images, analyzing performance and runtime throughput that determine real-world usability. Overall, by relying only on commodity cameras and hardware, and by open-sourcing all code, models, and datasets, this work aims to serve as a reference for real-time trading card recognition and to make real-time augmented-reality streaming accessible to the broader community of players and streamers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
