Abstract
Abstract. Large-scale pretrained language models have been a revolution in human-machine communication. Recently, such language models also generate code for required tasks. The objective of this work is to evaluate the functionality of the codes generated by ChatGPT (version 15-Dec-2022) for point cloud processing. The programming language selected for the test was MATLAB due to the extensive use in prototyping and toolboxes for Computer Vision and LiDAR. Using the Question-Answer system, the ChatGPT was asked for codes to calculate surface normals, curvature, eigenvalues, and eigenfeatures, with specific parameters and outputs. The provided codes were compiled and executed. The results show that ChatGPT can generate functional code for very specific and short applications, however, it is not capable of generating large code involving the correct use of loops, indexes, or equations.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
