# Semantic Scene Segmentation for Robotics Applications

arXiv: https://arxiv.org/abs/2108.11128 · HTML: https://mlsanity.com/paper/2108.11128/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 20 of 20)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation](https://mlsanity.com/paper/2004.02147/) (2020)
- [Hierarchical Multi-Scale Attention for Semantic Segmentation](https://mlsanity.com/paper/2005.10821/) (2020)
- [ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation](https://mlsanity.com/paper/1803.06815/) (2018)
- [Fast-SCNN: Fast Semantic Segmentation Network](https://mlsanity.com/paper/1902.04502/) (2019)
- [Improving Semantic Segmentation via Decoupled Body and Edge Supervision](https://mlsanity.com/paper/2007.10035/) (2020)
- [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://mlsanity.com/paper/2101.06085/) (2021)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)
- [FasterSeg: Searching for Faster Real-time Semantic Segmentation](https://mlsanity.com/paper/1912.10917/) (2019)
- [HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation](https://mlsanity.com/paper/2012.11582/) (2020)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1905.02423/) (2019)
- [A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images](https://mlsanity.com/paper/1912.10230/) (2019)
- [LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation](https://mlsanity.com/paper/1912.06683/) (2019)

## Built on by (most cited first, 7 of 7)

- [FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models](https://mlsanity.com/paper/2403.20105/) (2024)
- [Contextrast: Contextual Contrastive Learning for Semantic Segmentation](https://mlsanity.com/paper/2404.10633/) (2024)
- [AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model](https://mlsanity.com/paper/2410.09714/) (2024)
- [ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI](https://mlsanity.com/paper/2201.10366/) (2022)
- [OVeNet: Offset Vector Network for Semantic Segmentation](https://mlsanity.com/paper/2303.14516/) (2023)
- [Semi-Supervised Semantic Segmentation via Derivative Label Propagation](https://mlsanity.com/paper/2508.02254/) (2025)
- [Contextrast++: Robust Multi-Scale Contextual Contrastive Learning for Semantic Segmentation](https://mlsanity.com/paper/2608.22679/) (2026)

## 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.