Real‐time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre‐processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high‐quality filtering operations in such scenarios, we propose an output‐sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on‐the‐fly processing.

中文翻译：

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流式体积数据的输出敏感过滤

实时体积数据采集对传统的可视化管道提出了重大挑战，其中数据增强通常被视为预处理步骤。例如，在 4D 超声数据的情况下，需要对每一帧执行昂贵的处理操作以减少噪声和去除伪影。为了能够在这种情况下使用高质量的过滤操作，我们提出了一种输出敏感的方法来可视化流式体积数据。我们的方法评估所有体素对最终图像的潜在贡献，允许我们跳过对可视化影响很小或没有影响的昂贵处理操作。当过滤操作修改可能影响可见性的数据值时，我们的主要贡献是一种快速方案来预测它们对最终图像的最大影响。我们的方法基于每个像素的最大允许误差，优先过滤对最终可视化有很大贡献的体素。在允许误差为零的情况下，优化的过滤将产生与整个体积的过滤相同的结果。我们对该方法进行了全面的技术评估，并在几个需要即时处理的典型场景中对其进行了演示。