Omni-Scan: Creating Visually-Accurate Digital Twin Object Models Using a Bimanual Robot with Handover and Gaussian Splat Merging

TL;DR

Omni-Scan introduces a robot-based pipeline that captures comprehensive 3D Gaussian Splat models of objects using a bi-manual robot to handle occlusions, enabling high-quality digital twins for various applications.

Contribution

The paper presents a novel robotic scanning method that combines multi-view data with occlusion handling to produce accurate 3D Gaussian Splat models.

Findings

Achieved 83% average accuracy in defect detection across 12 objects.

Enabled 360-degree object modeling with a bi-manual robot setup.

Supported high-quality 3D model generation from occluded views.

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 https://berkeleyautomation.github.io/omni-scan/