Does a Dense Point Cloud for Training Data Generation Improve Segmentation Accuracy?

Semantic segmentation can provide significant information for a robot to actualize autonomous moving. To increase the classification accuracy of segmentation, appropriate datasets should be prepared, which requires huge human labors. The authors attempt to realize a semi-automatic method that generates two dimensional images having pixel-wise class labels from 3D point clouds obtained by a 3D scanner. In this study, dense point clouds are generated to improve the quality of training samples. Experimental results using data obtained around the course of the Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that the dense point clouds effective but appropriate variety of textures should be included in the training samples.