SSP3D5000: a synthetic dataset for ship 3D perception

Abstract

Currently in the field of ship perception, datasets lack 3D information fusing images and point clouds, and the real dataset faces difficulties such as collecting data in extreme working conditions and the low accuracy of data labeling. In this paper, a synthesized dataset SSP3D5000 for 3D perception of ships is constructed by virtual synthesis technology. The dataset contains three data types, binocular image, depth image and point cloud. 5000 sets of binocular and depth images containing 90 ship models and 325 accompanying point cloud data are acquired for different factors such as ambient lighting, weather, viewing angles and sea surface. SSP3D5000 provides 12-dimensional labeling information including categories and 2D/3D bounding boxes. Virtual images and point clouds are evaluated using a variety of imagebased 2D detection and point cloud-based 3D detection baseline models. The evaluation results show the feasibility and effectiveness of synthetic data in the field of ship perception sensing, which can help in the realization of various computer vision tasks for ship perception.