This paper presents a novel general-purpose stereo and depth data fusion paradigm that mimics the active stereo principle by replacing the unreliable physical pattern projector with a depth sensor. It works by projecting virtual patterns consistent with the scene geometry onto the left and right images acquired by a conventional stereo camera, using the sparse hints obtained from a depth sensor, to facilitate the visual correspondence. Purposely, any depth sensing device can be seamlessly plugged into our framework, enabling the deployment of a virtual active stereo setup in any possible environment and overcoming the severe limitations of physical pattern projection, such as the limited working range and environmental conditions. Exhaustive experiments on indoor and outdoor datasets featuring both long and close range, including those providing raw, unfiltered depth hints from off-the-shelf depth sensors, highlight the effectiveness of our approach in notably boosting the robustness and accuracy of algorithms and deep stereo without any code modification and even without re-training. Additionally, we assess the performance of our strategy on active stereo evaluation datasets with conventional pattern projection. Indeed, in all these scenarios, our virtual pattern projection paradigm achieves state-of-the-art performance. The source code is available at: https://github.com/bartn8/vppstereo.

该研究提出了一种新颖的通用立体和深度数据融合范例，通过用深度传感器替代不可靠的物理模式投影仪模拟主动立体原理。它通过将与场景几何一致的虚拟模式投影到由传统立体相机获取的左右图像上，利用深度传感器获得的稀疏提示，促进视觉对应。我们的框架可以与任何深度感应设备无缝连接，实现虚拟主动立体设置的部署，克服了物理模式投影的严重限制，如有限的工作范围和环境条件。通过在室内和室外数据集上进行详尽实验，包括提供来自现成深度传感器的原始、未滤波的深度提示的长距离和近距离数据，突出了我们方法在不修改代码甚至无需重新训练的情况下显著提升算法和深度立体的鲁棒性和准确性。此外，我们评估了我们策略在传统模式投影的主动立体评估数据集上的性能。实际上，在所有这些场景中，我们的虚拟模式投影范例实现了最先进的性能。源代码可在此https URL找到。

虚拟图案投射的立体深度融合