mlsanityData to 7 Oct 2026

Vision-based Large-scale 3D Semantic Mapping for Autonomous Driving Applications

arXiv 2203.01087 ↗First posted 2 Mar 2022cs.CV cs.AIcited by 4

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Abstract (arXiv)

In this paper, we present a complete pipeline for 3D semantic mapping solely based on a stereo camera system. The pipeline comprises a direct sparse visual odometry front-end as well as a back-end for global optimization including GNSS integration, and semantic 3D point cloud labeling. We propose a simple but effective temporal voting scheme which improves the quality and consistency of the 3D point labels. Qualitative and quantitative evaluations of our pipeline are performed on the KITTI-360 dataset. The results show the effectiveness of our proposed voting scheme and the capability of our pipeline for efficient large-scale 3D semantic mapping. The large-scale mapping capabilities of our pipeline is furthermore demonstrated by presenting a very large-scale semantic map covering 8000 km of roads generated from data collected by a fleet of vehicles.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 44 of its 63 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

44 of 63 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.

4 papers.

  1. 2024SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Modelscited by 28
  2. 2024Neural Semantic Map-Learning for Autonomous Vehiclescited by 1
  3. 2023Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localizationcited by 0
  4. 2026VDGS: Visibility-Driven Large-Scale 3D Gaussian Splatting for Aerial Scene Reconstructioncited by 0