  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.
