Depth Completion with RGB Prior
This work addresses depth perception challenges for robotics in industrial environments, presenting an incremental improvement with a new dataset.
The paper tackled the problem of depth camera inaccuracies caused by reflective objects and harsh lighting in industrial settings by developing a deep model to correct depth channels in RGBD images, achieving restored depth accuracy as demonstrated on a novel industrial dataset.
Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments. Industrial applications, however, typically involve reflective objects under harsh lighting conditions, a challenging scenario for depth cameras, as it induces numerous reflections and deflections, leading to loss of robustness and deteriorated accuracy. Here, we developed a deep model to correct the depth channel in RGBD images, aiming to restore the depth information to the required accuracy. To train the model, we created a novel industrial dataset that we now present to the public. The data was collected with low-end depth cameras and the ground truth depth was generated by multi-view fusion.