Efficient visibility computation is a prominent requirement when designing automated camera control techniques for dynamic 3D environments; computer games, interactive storytelling or 3D media applications all need to track 3D entities while ensuring their visibility and delivering a smooth cinematic experience. Addressing this problem requires to sample a large set of potential camera positions and estimate visibility for each of them, which in practice is intractable despite the efficiency of ray‐casting techniques on recent platforms. In this work, we introduce a novel GPU‐rendering technique to efficiently compute occlusions of tracked targets in Toric Space coordinates – a parametric space designed for cinematic camera control. We then rely on this occlusion evaluation to derive an anticipation map predicting occlusions for a continuous set of cameras over a user‐defined time window. We finally design a camera motion strategy exploiting this anticipation map to minimize the occlusions of tracked entities over time. The key features of our approach are demonstrated through comparison with traditionally used ray‐casting on benchmark scenes, and through an integration in multiple game‐like 3D scenes with heavy, sparse and dense occluders.

中文翻译：

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环面空间自动相机控制的遮挡实时预测

在为动态 3D 环境设计自动相机控制技术时，高效的可见性计算是一个突出的要求；计算机游戏、交互式故事讲述或 3D 媒体应用程序都需要跟踪 3D 实体，同时确保它们的可见性并提供流畅的电影体验。解决这个问题需要对大量潜在的相机位置进行采样并估计每个位置的可见性，尽管在最近的平台上光线投射技术的效率很高，但这在实践中是棘手的。在这项工作中，我们引入了一种新颖的 GPU 渲染技术，以有效计算 Toric 空间坐标中跟踪目标的遮挡 - 一个专为电影摄影机控制设计的参数空间。然后，我们依靠这种遮挡评估来推导出一个预期图，该图预测用户定义的时间窗口内一组连续相机的遮挡。我们最终设计了一种利用此预期图的相机运动策略，以最小化跟踪实体随时间的遮挡。通过与基准场景上传统使用的光线投射进行比较，以及通过与重、稀疏和密集遮挡物的多个类似游戏的 3D 场景的集成，证明了我们方法的关键特征。