Precise Workcell Sketching from Point Clouds Using an AR Toolbox

TL;DR

This paper introduces an AR-based toolbox for sketching and refining 3D workcell models from point clouds, combining the efficiency of raw data capture with the precision of parametric CAD representations.

Contribution

It presents a novel AR interface that allows users to improve point cloud accuracy interactively, bridging the gap between raw sensor data and detailed CAD models.

Findings

Achieves mean error within 1cm compared to ground truth.

Significantly improves over standard LiDAR scanner apps.

Enables efficient, user-guided 3D workcell modeling.

Abstract

Capturing real-world 3D spaces as point clouds is efficient and descriptive, but it comes with sensor errors and lacks object parametrization. These limitations render point clouds unsuitable for various real-world applications, such as robot programming, without extensive post-processing (e.g., outlier removal, semantic segmentation). On the other hand, CAD modeling provides high-quality, parametric representations of 3D space with embedded semantic data, but requires manual component creation that is time-consuming and costly. To address these challenges, we propose a novel solution that combines the strengths of both approaches. Our method for 3D workcell sketching from point clouds allows users to refine raw point clouds using an Augmented Reality (AR) interface that leverages their knowledge and the real-world 3D environment. By utilizing a toolbox and an AR-enabled pointing device, users can enhance point cloud accuracy based on the device's position in 3D space. We validate our approach by comparing it with ground truth models, demonstrating that it achieves a mean error within 1cm - significant improvement over standard LiDAR scanner apps.