mlsanityData to 7 Oct 2026

Semantic Scene Segmentation for Robotics Applications

arXiv 2108.11128 ↗First posted 25 Aug 2021cs.CVcited by 7

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Abstract (arXiv)

Semantic scene segmentation plays a critical role in a wide range of robotics applications, e.g., autonomous navigation. These applications are accompanied by specific computational restrictions, e.g., operation on low-power GPUs, at sufficient speed, and also for high-resolution input. Existing state-of-the-art segmentation models provide evaluation results under different setups and mainly considering high-power GPUs. In this paper, we investigate the behavior of the most successful semantic scene segmentation models, in terms of deployment (inference) speed, under various setups (GPUs, input sizes, etc.) in the context of robotics applications. The target of this work is to provide a comparative study of current state-of-the-art segmentation models so as to select the most compliant with the robotics applications requirements.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 25 Aug 2021.

Builds on

20 of 28 references.

References · 20

  1. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  3. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  4. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,063
  5. 2016COCO-Stuff: Thing and Stuff Classes in Contextcited by 858
  6. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  7. 2019Segmentation Transformer: Object-Contextual Representations for Semantic Segmentationcited by 573
  8. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
  9. 2020Hierarchical Multi-Scale Attention for Semantic Segmentationcited by 183
  10. 2018ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentationcited by 133
  11. 2019Fast-SCNN: Fast Semantic Segmentation Networkcited by 124
  12. 2020Improving Semantic Segmentation via Decoupled Body and Edge Supervisioncited by 98
8 more
  1. 2021Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenescited by 89
  2. 2019HarDNet: A Low Memory Traffic Networkcited by 76
  3. 2019FasterSeg: Searching for Faster Real-time Semantic Segmentationcited by 73
  4. 2020HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentationcited by 66
  5. 2017Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNscited by 55
  6. 2019LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentationcited by 50
  7. 2019A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D imagescited by 29
  8. 2019LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentationcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 1; 2026: 1.

7 papers.

  1. 2024FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Modelscited by 12
  2. 2024Contextrast: Contextual Contrastive Learning for Semantic Segmentationcited by 12
  3. 2024AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Modelcited by 3
  4. 2022ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AIcited by 1
  5. 2023OVeNet: Offset Vector Network for Semantic Segmentationcited by 1
  6. 2025Semi-Supervised Semantic Segmentation via Derivative Label Propagationcited by 0
  7. 2026Contextrast++: Robust Multi-Scale Contextual Contrastive Learning for Semantic Segmentationcited by 0