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Depth Completion for Close-Range Specular Objects

Many objects in the real world exhibit specular reflections. Due to the limitations of the basic RGB-D cameras, it is particularly challenging to accurately capture their 3D shapes. In this work, we present an approach to correct the depth of close-range specular objects using convolutional neural networks. We first generate a synthetic dataset containing such close-range objects. We then train a deep convolutional network to estimate normal and boundary maps from a single image.With these results, we propose an algorithm to detect the incorrect area of the raw depth map. After removing the erroneous zone, we complete the depth channel.