We have compared the sampling methods quantitatively using root mean squared error (RMSE) and Euler angles; in addition, we adapt a form of quantitative evalua-.

Improved Cloud Partitioning Sampling for Iterative Closest Point: Qualitative and Quantitative Comparison Study. January 2018. DOI:10.5220/0006828500490060.

Improved Cloud Partitioning Sampling for Iterative Closest Point: Qualitative and Quantitative Comparison Study. Polycarpo Souza Neto, Nicolas S. Pereira ...

Apr 9, 2022 · In this work, we introduce a cloud-partitioning strategy for improved registration and compare it to other relevant approaches by using both time and quality ...

Apr 22, 2024 · In this paper, we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm, two typical ...

Jun 25, 2024 · In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite-sector clustering.

This paper proposes a point-by-point weighted fusion algorithm based on an improved random sample consensus (RANSAC) and inverse distance weighting.

In this approach, the dense point cloud undergoes iterative subsampling to diminish its spatial resolution, while a neural network is concurrently applied to ...

Oct 22, 2024 · Each ground-truth point cloud contains 500,000 points, obtained from Uniform Sampling. Our method necessitates sampling an even larger number of ...

Jan 12, 2016 · In this article, an accurate method for the registration of point clouds returned by a 3D rangefinder is presented.