Minimizing Supervision for Free-space Segmentation

TL;DR

This paper presents a new weakly supervised framework for free-space segmentation in autonomous driving, leveraging texture and location cues to reduce manual annotation costs and improve performance.

Contribution

The authors introduce a novel framework that effectively segments free-space with minimal supervision by exploiting texture and positional features.

Findings

Outperforms existing weakly supervised methods

Requires less human supervision

Demonstrates potential for adaptable autonomous navigation

Abstract

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