mlsanityData to 14 Oct 2026

OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments

arXiv 2106.13963 ↗First posted 26 Jun 2021cs.CV cs.ROcited by 4

Not on the map. Why?

Abstract (arXiv)

We present OffRoadTranSeg, the first end-to-end framework for semi-supervised segmentation in unstructured outdoor environment using transformers and automatic data selection for labelling. The offroad segmentation is a scene understanding approach that is widely used in autonomous driving. The popular offroad segmentation method is to use fully connected convolution layers and large labelled data, however, due to class imbalance, there will be several mismatches and also some classes may not be detected. Our approach is to do the task of offroad segmentation in a semi-supervised manner. The aim is to provide a model where self supervised vision transformer is used to fine-tune offroad datasets with self-supervised data collection for labelling using depth estimation. The proposed method is validated on RELLIS-3D and RUGD offroad datasets. The experiments show that OffRoadTranSeg outperformed other state of the art models, and also solves the RELLIS-3D class imbalance problem.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 23 references that are in the map. First posted on arXiv 26 Jun 2021.

Builds on

18 of 23 references.

References · 18

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6 more
  1. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  2. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
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  6. 2021OFFSEG: A Semantic Segmentation Framework For Off-Road Drivingcited by 20

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Cited by year

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

4 papers.

  1. 2022VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terraincited by 8
  2. 2025Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarkscited by 8
  3. 2023CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learningcited by 7
  4. 2024Autonomous Driving in Unstructured Environments: How Far Have We Come?cited by 7