  Identifying "free-space," or safely driveable regions in the scene ahead, is
a fundamental task for autonomous navigation. While this task can be addressed
using semantic segmentation, the manual labor involved in creating pixelwise
annotations to train the segmentation model is very costly. Although weakly
supervised segmentation addresses this issue, most methods are not designed for
free-space. In this paper, we observe that homogeneous texture and location are
two key characteristics of free-space, and develop a novel, practical framework
for free-space segmentation with minimal human supervision. Our experiments
show that our framework performs better than other weakly supervised methods
while using less supervision. Our work demonstrates the potential for
performing free-space segmentation without tedious and costly manual
annotation, which will be important for adapting autonomous driving systems to
different types of vehicles and environments
