# Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments Source: [Few-shot Semantic Learning for Robust Multi-Biome 3D Semantic Mapping in Off-Road Environments]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:47:55.951Z Authors: Deegan Atha, Xianmei Lei, Shehryar Khattak, Anna Sabel, Elle Miller, Aurelio Noca, Grace Lim, Jeffrey Edlund, Curtis Padgett, Patrick Spieler Published: 2024-11-10T23:52:24.000Z ## Abstract Off-road environments pose significant perception challenges for high-speed autonomous navigation due to unstructured terrain, degraded sensing conditions, and domain-shifts among biomes. Learning semantic information across these conditions and biomes can be challenging when a large amount of ground truth data is required. In this work, we propose an approach that leverages a pre-trained Vision Transformer (ViT) with fine-tuning on a small (\<500 images), sparse and coarsely labeled (\<30% pixels) multi-biome dataset to predict 2D semantic segmentation classes. These classes are fused over time via a novel range-based metric and aggregated into a 3D semantic voxel map. We demonstrate zero-shot out-of-biome 2D semantic segmentation on the Yamaha (52.9 mIoU) and Rellis (55.5 mIoU) datasets along with few-shot coarse sparse labeling with existing data for improved segmentation performance on Yamaha (66.6 mIoU) and Rellis (67.2 mIoU). We further illustrate the feasibility of using a voxel map with a range-based semantic fusion approach to handle common off-road hazards like pop-up hazards, overhangs, and water features. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()