Conf-Net: Toward High-Confidence Dense 3D Point-Cloud with Error-Map Prediction

This work proposes a method for depth completion of sparse LiDAR data using a convolutional neural network which can be used to generate semi-dense depth maps and 3D point-clouds with significantly lower root mean squared error (RMSE) over state-of-the-art methods.