Summary of Few-shot Semantic Learning For Robust Multi-biome 3d Semantic Mapping in Off-road Environments, by Deegan Atha et al.
Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments
by Deegan Atha, Xianmei Lei, Shehryar Khattak, Anna Sabel, Elle Miller, Aurelio Noca, Grace Lim, Jeffrey Edlund, Curtis Padgett, Patrick Spieler
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach uses a pre-trained Vision Transformer (ViT) fine-tuned on a small, sparse dataset to predict 2D semantic segmentation classes for high-speed autonomous navigation in off-road environments. The method fuses these classes over time using a novel range-based metric and aggregates them into a 3D semantic voxel map. The approach demonstrates zero-shot out-of-biome 2D semantic segmentation on the Yamaha and Rellis datasets, as well as few-shot coarse sparse labeling for improved segmentation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-road environments are hard to navigate because of rough terrain, bad weather, and different types of landscapes. To make self-driving cars work better in these situations, researchers need a lot of ground truth data. However, this data can be difficult to collect and label. This paper proposes a new way to use a pre-trained model called ViT to predict what’s in the scene without needing as much labeled data. The method works by predicting 2D semantic segmentation classes and then combining them over time using a special metric. It also creates a 3D map of the scene, which can help detect hazards like rocks or water. |
Keywords
» Artificial intelligence » Few shot » Semantic segmentation » Vision transformer » Vit » Zero shot
