Deformable spatial propagation network for depth completion

Understand

Depth completion has attracted extensive attention recently due to the development of autonomous driving, which aims to recover dense depth map from sparse depth measurements.

• Convolutional spatial propagation network (CSPN) is one of the state-of-the-art methods in this task, which adopt a linear propagation model to refine coarse depth maps with local context.
• However, the propagation of each pixel occurs in a fixed receptive field.
• This may not be the optimal for refinement since different pixel needs different local context.