A Comprehensive View-Dependent Dataset for Objects Reconstruction - Synthetic Set Part A

Data were collected in the custom simulator that loads random graspable objects from the ShapeNet dataset and random table. The graspable object is placed above the table in a random position. Then, the scene is simulated using the PhysX engine to make sure that the scene is physically plausible. The simulator captures images of the scene from a random pose and then takes the second image from the camera pose that is on the opposite side of the scene. It contains RGB, depth, segmentation images of the scenes and objects on the scene and information about the camera poses that can be used to create a full 3D model of the scene and develop methods that reconstruct objects from a single RGB-D camera view. Part A of the dataset contains four categories: a bottle, a box, a can, and a cube.

本研究数据集的构建基于一款定制化的模拟器，该模拟器从 ShapeNet 数据集及随机选取的桌面场景中加载可抓取的物体。可抓取的物体被随机放置于桌面之上。随后，利用 PhysX 引擎对该场景进行模拟，以确保物理现实性。模拟器从随机角度捕捉场景图像，并从场景相反侧的相机姿态获取第二张图像。数据集包含场景及场景中物体的 RGB 图像、深度图像、分割图像，以及可用于构建场景完整 3D 模型并开发从单一 RGB-D 相机视图重建物体方法之相机姿态信息。数据集的 Part A 部分分为四大类别：瓶装、盒状、罐装及立方体。