论文标题
RGB先验完成深度完成
Depth Completion with RGB Prior
论文作者
论文摘要
深度摄像机是机器人技术的突出感知系统，尤其是在自然的非结构化环境中运行时。但是，工业应用通常涉及在恶劣的照明条件下的反射物体，这是深度摄像机的挑战性情况，因为它引起了许多反射和偏转，从而导致稳健性丧失和准确性降低。在这里，我们开发了一个深层模型，以纠正RGBD图像中的深度通道，旨在将深度信息恢复到所需的准确性。为了训练该模型，我们创建了一个新颖的工业数据集，现在向公众展示。数据是用低端深度摄像机收集的，地面真实深度是由多视图融合产生的。
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.
