RoCap: A Robotic Data Collection Pipeline for the Pose Estimation of Appearance-Changing Objects

TL;DR

Rocap is a robotic data collection pipeline that captures images of objects in various poses to improve pose estimation for appearance-changing objects, outperforming traditional synthetic data methods.

Contribution

The paper introduces Rocap, a novel robotic pipeline for generating labeled data for pose estimation of appearance-changing objects, addressing limitations of static object methods.

Findings

Rocap effectively captures diverse object poses with ground truth labels.

Models trained on Rocap data outperform synthetic data-based models.

The approach improves pose estimation accuracy for deformable, transparent, reflective, and articulated objects.

Abstract

Object pose estimation plays a vital role in mixed-reality interactions when users manipulate tangible objects as controllers. Traditional vision-based object pose estimation methods leverage 3D reconstruction to synthesize training data. However, these methods are designed for static objects with diffuse colors and do not work well for objects that change their appearance during manipulation, such as deformable objects like plush toys, transparent objects like chemical flasks, reflective objects like metal pitchers, and articulated objects like scissors. To address this limitation, we propose Rocap, a robotic pipeline that emulates human manipulation of target objects while generating data labeled with ground truth pose information. The user first gives the target object to a robotic arm, and the system captures many pictures of the object in various 6D configurations. The system trains a model by using captured images and their ground truth pose information automatically calculated from the joint angles of the robotic arm. We showcase pose estimation for appearance-changing objects by training simple deep-learning models using the collected data and comparing the results with a model trained with synthetic data based on 3D reconstruction via quantitative and qualitative evaluation. The findings underscore the promising capabilities of Rocap.