  Relying on deep supervised or self-supervised learning, previous methods for
depth completion from paired single image and sparse depth data have achieved
impressive performance in recent years. However, facing a new environment where
the test data occurs online and differs from the training data in the RGB image
content and depth sparsity, the trained model might suffer severe performance
drop. To encourage the trained model to work well in such conditions, we expect
it to be capable of adapting to the new environment continuously and
effectively. To achieve this, we propose MetaComp. It utilizes the
meta-learning technique to simulate adaptation policies during the training
phase, and then adapts the model to new environments in a self-supervised
manner in testing. Considering that the input is multi-modal data, it would be
challenging to adapt a model to variations in two modalities simultaneously,
due to significant differences in structure and form of the two modal data.
Therefore, we further propose to disentangle the adaptation procedure in the
basic meta-learning training into two steps, the first one focusing on the
depth sparsity while the second attending to the image content. During testing,
we take the same strategy to adapt the model online to new multi-modal data.
Experimental results and comprehensive ablations show that our MetaComp is
capable of adapting to the depth completion in a new environment effectively
and robust to changes in different modalities.
