  As one of the fundamental functions of autonomous driving system, freespace
detection aims at classifying each pixel of the image captured by the camera as
drivable or non-drivable. Current works of freespace detection heavily rely on
large amount of densely labeled training data for accuracy and robustness,
which is time-consuming and laborious to collect and annotate. To the best of
our knowledge, we are the first work to explore unsupervised domain adaptation
for freespace detection to alleviate the data limitation problem with synthetic
data. We develop a cross-modality domain adaptation framework which exploits
both RGB images and surface normal maps generated from depth images. A
Collaborative Cross Guidance (CCG) module is proposed to leverage the context
information of one modality to guide the other modality in a cross manner, thus
realizing inter-modality intra-domain complement. To better bridge the domain
gap between source domain (synthetic data) and target domain (real-world data),
we also propose a Selective Feature Alignment (SFA) module which only aligns
the features of consistent foreground area between the two domains, thus
realizing inter-domain intra-modality adaptation. Extensive experiments are
conducted by adapting three different synthetic datasets to one real-world
dataset for freespace detection respectively. Our method performs closely to
fully supervised freespace detection methods (93.08 v.s. 97.50 F1 score) and
outperforms other general unsupervised domain adaptation methods for semantic
segmentation with large margins, which shows the promising potential of domain
adaptation for freespace detection.

Computer Vision and Pattern Recognition;Artificial Intelligence