An Adaptive Sampling Approach to 3D Reconstruction of Weld Joint

TL;DR

This paper introduces an adaptive sampling method for 3D welding joint reconstruction from laser point clouds, significantly reducing sample numbers and computation time compared to random sampling.

Contribution

The paper proposes a novel adaptive sampling algorithm that improves 3D reconstruction efficiency by selectively refining the sampling process around pivotal points.

Findings

Adaptive sampling outperforms random sampling in sample efficiency

Reduces total samples needed for accurate 3D reconstruction

Shortens computation time for welding joint modeling

Abstract

We present an adaptive sampling approach to 3D reconstruction of the welding joint using the point cloud that is generated by a laser sensor. We start with a randomized strategy to approximate the surface of the volume of interest through selection of a number of pivotal candidates. Furthermore, we introduce three proposal distributions over the neighborhood of each of these pivots to adaptively sample from their neighbors to refine the original randomized approximation to incrementally reconstruct this welding space. We prevent our algorithm from being trapped in a neighborhood via permanently labeling the visited samples. In addition, we accumulate the accepted candidates along with their selected neighbors in a queue structure to allow every selected sample to contribute to the evolution of the reconstructed welding space as the algorithm progresses. We analyze the performance of our adaptive sampling algorithm in contrast to the random sampling, with and without replacement, to show a significant improvement in total number of samples that are drawn to identify the region of interest, thereby expanding upon neighboring samples to extract the entire region in a fewer iterations and a shorter computation time.