Comment on: “A generalized weighted total least squares-based, iterative solution to the estimation of 3D similarity transformation parameters” by Wang et al. (2023)

• Published in: Measurement : journal of the International Measurement Confederation Vol. 244; p. 116521
• Main Author: Bektas, Sebahattin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 28.02.2025
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.116521

A recent paper by Wang et al. (2023) A generalized weighted total least squares-based, iterative solution to the estimation of 3D similarity transformation parameters, Measurement 210 (2023) 112563, https://doi.org/10.1016/j.measurement.2023.112563 on 3D symmetric similarity coordinate transformations based on a generalized weighted total least squares. I found the results were not entirely accurate. For control purposes, 3 separate data sets in the article (Table 2, Table 5 and Table 8) were solved according to Bektas (2024). The results are exactly the same as Bektas (2024) and Mercan et al. (2018). Wang et al. (2023) results contain minor differences. The transformation parameters found were not completely correct and there were significant differences, especially in the residuals. My guess is that the differences in Wang et al.’s (2023) results are due to poor convergence, the authors should have looked for ways to deal with poor conditioning, but they didn’t.