Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study

Round 1

Reviewer 1 Report

This paper researched computer vision algorithms review for 3D object recognition and orientation, the topic of this paper is interesting and important, and I also saw that the author has done in-depth analysis and collation of the latest frameworks,whereas this paper has the following minor problems that require further revision: The paper reviews several aspects of 3D object recognition and orientation  such as autonomous driving, point cloud, robotics, and LiDAR. This is a good idea, but deep learning for autonomous driving is not only the application of point cloud processing, but also includes camera and machine vision based on deep learning for target recognition, so I suggest the author conduct a technical classification review in this area; Besides, Figure 5.Topic dendrogram is not clear.

There are minor grammatical issues in this paper that need to be revised.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

The paper studied the research situation of 3D object detection. The authors introduced the five meticulous phases in the bibliometric study, conducted the bibliometric analysis, and presented some conclusions. The paper seems lack of innovations although some findings were summarized.

1 During the bibliometric study, we need the databases and the keywords to search the papers, and carry out data analysis. So in Section 3 the five meticulous phases have no special technology.

2 The bibliometric analysis in Section 4 can be achieved by using some developed programs. The great contributions of authors have not been found.

3 Section 5 introduced 8 papers and just simply reviewed what they did. 

4 The paper aimed at a comprehensive bibliometric analysis on the topic of 3D object detection from 2022 to 2023. However, why were the papers published before 2022 in Table 5 and 6 selected?

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

This paper focuses on a bibliometric study of 3D object detection. The research perspective of this article is innovative. It can provide some directions for research in object detection. However, some modifications should be made before publication.

1) As you have mentioned, 3D object detection has wide applications on object detection. It would be meaningful to include the related work: secure cooperative localization for connected automated vehicles based on consensus, automated vehicle sideslip angle estimation considering signal measurement characteristic, integrated inertial-lidar-based map matching localization for varying environments. One of the reasons is that it could help vehicle localization and state estimation.

2) For the 3D-based Lidar detection, the deep learning methods could be divided into the following two parts: CNN-based methods, and Transformer-based methods, such as: hydro-3d: hybrid object detection and tracking for cooperative perception using 3d lidar. Especially, some transformer-based work should be discussed.

3) It would be better to highlight the work contribution at the end of the introduction.

4) It would be meaningful to provide some challenges for the future direction.

5) It is different to distinguish the countries from Figure 8. Please optimize it.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Words in Fig.2 , Fig.5 and Fig.7 are too small and are difficult for readers to read.

Author Response

Thank you very much for the care and detail. Figures corrected according to the suggestion!