Pre-training on urban LiDAR datasets and then fine-tuning a small classifier on 37 labeled scans raises unimproved-road segmentation mIoU from 33.5% to 51.8%, though the credit to multi-dataset training is not cleanly isolated.
SemanticKITTI: A Dataset for Semantic Scene Under- standing of LiDAR Sequences,
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Data-Efficient Point Cloud Semantic Segmentation Pipeline for Unimproved Roads
Pre-training on urban LiDAR datasets and then fine-tuning a small classifier on 37 labeled scans raises unimproved-road segmentation mIoU from 33.5% to 51.8%, though the credit to multi-dataset training is not cleanly isolated.