Survey of semantic segmentation for autonomous driving including efficiency and use of depth or time
Survey of semantic segmentation for autonomous driving including efficiency and use of depth or time
A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design
arXiv paper abstract https://arxiv.org/abs/2303.04315
arXiv PDF paper https://arxiv.org/pdf/2303.04315.pdf
Semantic image and video segmentation … provide a … 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 … performance
… 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.
… 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
… 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.
… objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective …
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