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This paper introduces two new multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization, one dense and one sparse, collected in Fukuoka, Japan. The dense dataset contains 650 static panoramic scans from a FARO scanner, while the sparse dataset comprises 34,200 real-time panoramic scans from a Velodyne scanner mounted on a car. Benchmarking experiments on these datasets achieve semantic place categorization accuracies of 96.42% (dense) and 89.67% (sparse), establishing baseline performance.
Two new datasets of panoramic 3D scans promise to accelerate research in semantic place categorization, offering both high-density static and low-density mobile capture modalities.
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).