RGBD-FG: A Large-Scale RGB-D Dataset for Fine-Grained Categorization

Fine-grained visual categorization (FGVC) has received a great deal of attention in recent years. Currently, several public datasets are available for FGVC. However, all these datasets were created with RGB images. RGB-D sensors can provide high-quality synchronized video in terms of both color and depth. In this paper, we introduce a multi-view, large-scale RGB-D dataset called RGBD-FG to establish a novel benchmark for FGVC in RGB-D images. RGBD-FG contains RGB data and the corresponding depth data of vegetables and fruits. Our dataset was captured by a depth sensor and contained 50 categories and a total of 93,051 RGB-D images with labels, and organized in a hierarchical manner. Additionally, we used several strategies on our dataset for FGVC, including a multi-modal deep CNN. We present extensive experimental results to create state-of-the-art baselines for the dataset. We hope that this dataset can fill the gap of FGVC among the RGB-D datasets.