  Freespace detection is an essential component of visual perception for
self-driving cars. The recent efforts made in data-fusion convolutional neural
networks (CNNs) have significantly improved semantic driving scene
segmentation. Freespace can be hypothesized as a ground plane, on which the
points have similar surface normals. Hence, in this paper, we first introduce a
novel module, named surface normal estimator (SNE), which can infer surface
normal information from dense depth/disparity images with high accuracy and
efficiency. Furthermore, we propose a data-fusion CNN architecture, referred to
as RoadSeg, which can extract and fuse features from both RGB images and the
inferred surface normal information for accurate freespace detection. For
research purposes, we publish a large-scale synthetic freespace detection
dataset, named Ready-to-Drive (R2D) road dataset, collected under different
illumination and weather conditions. The experimental results demonstrate that
our proposed SNE module can benefit all the state-of-the-art CNNs for freespace
detection, and our SNE-RoadSeg achieves the best overall performance among
different datasets.
