  Freespace detection is a fundamental component of autonomous driving
perception. Recently, deep convolutional neural networks (DCNNs) have achieved
impressive performance for this task. In particular, SNE-RoadSeg, our
previously proposed method based on a surface normal estimator (SNE) and a
data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace
detection. However, SNE-RoadSeg is computationally intensive, and it is
difficult to execute in real time. To address this problem, we introduce
SNE-RoadSeg+, an upgraded version of SNE-RoadSeg. SNE-RoadSeg+ consists of 1)
SNE+, a module for more accurate surface normal estimation, and 2) RoadSeg+, a
data-fusion DCNN that can greatly minimize the trade-off between accuracy and
efficiency with the use of deep supervision. Extensive experimental results
have demonstrated the effectiveness of our SNE+ for surface normal estimation
and the superior performance of our SNE-RoadSeg+ over all other freespace
detection approaches. Specifically, our SNE-RoadSeg+ runs in real time, and
meanwhile, achieves the state-of-the-art performance on the KITTI road
benchmark. Our project page is at
https://www.sne-roadseg.site/sne-roadseg-plus.
