Propagating Confidences through CNNs for Sparse Data Regression

Understand

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras.

• Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance.
• To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task.
• We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers.