Multi-modal panoramic 3D outdoor datasets for place categorization
This provides new benchmark datasets for semantic place categorization in outdoor environments, but the contribution is incremental as it primarily offers new data with standard evaluation.
The authors introduce two multi-modal panoramic 3D outdoor datasets for place categorization with six categories, achieving best classification accuracies of 96.42% on dense scans and 89.67% on sparse scans.
We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse).