Abstract
3D Gaussian Splats (3DGSs) are 3D object models derived from multi-view images. Such "digital twins" are useful for simulations, virtual reality, marketing, robot policy fine-tuning, and part inspection. 3D object scanning usually requires multi-camera arrays, precise laser scanners, or robot wrist-mounted cameras, which have restricted workspaces. We propose Omni-Scan, a pipeline for producing high-quality 3D Gaussian Splat models using a bi-manual robot that grasps an object with one gripper and rotates the object with respect to a stationary camera. The object is then re-grasped by a second gripper to expose surfaces that were occluded by the first gripper. We present the Omni-Scan robot pipeline using DepthAny-thing, Segment Anything, as well as RAFT optical flow models to identify and isolate objects held by a robot gripper while removing the gripper and the background. We then modify the 3DGS training pipeline to support concatenated datasets with gripper occlusion, producing an omni-directional (360 degree view) model of the object. We apply Omni-Scan to part defect inspection, finding that it can identify visual or geometric defects in 12 different industrial and household objects with an average accuracy of 83%. Interactive videos of Omni-Scan 3DGS models can be found at this https URL
Abstract (translated)
3D高斯点（3D Gaussian Splats，简称3DGS）是从多视角图像中生成的三维物体模型。这些“数字孪生”在仿真、虚拟现实、市场营销、机器人策略微调和部件检测等众多领域都非常有用。传统的三维物体扫描通常需要使用多个摄像头阵列、精密激光扫描仪或安装于机械臂腕部的相机，而后者的工作空间受到限制。 我们提出了一种名为Omni-Scan的新方法，该方法采用双臂机器人的抓取系统来生成高质量的3DGS模型。具体来说，一个机器人夹爪抓住物体并相对于固定摄像头旋转该对象，以使其不同侧面朝向摄像机；随后由第二个夹爪重新抓取同一物体，以便于先前被第一个夹爪遮挡的部分可以露出。我们利用DepthAny-thing、Segment Anything以及RAFT光流模型来识别和分离机器人夹持器所握的物件，并去除夹持器本身及背景部分。 在上述步骤完成后，我们会修改3DGS训练管道，以适应带有夹具遮挡的拼接数据集，最终生成一个全方位（360度视角）的对象模型。我们还利用Omni-Scan技术进行零件缺陷检测实验，在12种不同的工业和家用物品上实现了平均83%的准确率。 互动视频展示Omni-Scan 3DGS模型的具体应用可以访问以下链接：[此处插入实际URL]
URL
https://arxiv.org/abs/2508.00354
