SemanticSTF
Introduced by Xiao et al. in 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds
SemanticSTF is an adverse-weather point cloud dataset that provides dense point-level annotations and allows to study 3DSS under various adverse weather conditions. It contains 2,076 scans captured by a Velodyne HDL64 S3D LiDAR sensor from STF that cover various adverse weather conditions including 694 snowy, 637 dense-foggy, 631 light-foggy, and 114 rainy (all rainy LiDAR scans in STF).
Source: 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point CloudsPapers
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