Sparsity Invariant CNNs

Understand

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.

• First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network.
• To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation.
• We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches.