A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design

TL;DR

This paper reviews recent advances in deep semantic segmentation for autonomous vehicles, focusing on efficiency, depth integration, and temporal data to improve performance under real-world constraints.

Contribution

It provides a comprehensive survey of recent methods in efficiency-oriented models, RGB-D data integration, and temporally-aware segmentation for autonomous driving.

Findings

Efficiency-oriented models enable real-time segmentation.

RGB-D data improves scene understanding.

Temporal cues enhance segmentation consistency.

Abstract

Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Networks, have boosted semantic segmentation to a new level in terms of performance and generalization capabilities. However, designing Deep Semantic Segmentation models is a complex task, as it may involve application-dependent aspects. Particularly, when considering autonomous driving applications, the robustness-efficiency trade-off, as well as intrinsic limitations - computational/memory bounds and data-scarcity - and constraints - real-time inference - should be taken into consideration. In this respect, the use of additional data modalities, such as depth perception for reasoning on the geometry of a scene, and temporal cues from videos to explore redundancy and consistency, are promising directions yet not explored to their full potential in the literature. In this paper, we conduct a survey on the most relevant and recent advances in Deep Semantic Segmentation in the context of vision for autonomous vehicles, from three different perspectives: efficiency-oriented model development for real-time operation, RGB-Depth data integration (RGB-D semantic segmentation), and the use of temporal information from videos in temporally-aware models. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective, so that the reader can not only get started, but also be up to date in respect to recent advances in this exciting and challenging research field.