# 3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds
## 5 Conclusions and Future Works  
This paper proposed using Frustum ConvNet with an improved CBAM attention model for 3D object detection. We propose an improved attention module by adding Multilayer Perceptron (MLP) and using the tanh activation function to improve the contrast between the object and the surrounding environment. We evaluate the proposed Frustum ConvNet with the attention model (FCAM) in the KITTI dataset and achieve competitive results with the state-of-the-art methods. This Frustum ConvNet with attention architecture can provide applications such as autonomous driving and robotic object manipulation.  
In the future, we plan to further improve the performance of our 3D object detector. Our proposed attention model does not perform well when the network architecture is relatively complex. It is difficult for the attention model to focus on occluded objects in a complex environment. We plan to change the network architecture, reduce parameters, and further improve the adaptability of attention modules.  
**Author Contributions:** Conceptualization, Y.L.; data curation, Y.L.; formal analysis, Y.L.; funding acquisition, H.S.; investigation, Y.L.; methodology, Y.L. and H.X.; project administration, H.S.; resources, Y.L. and H.S.; software, Y.L.; supervision, H.X. and H.S.; validation, Y.L. and H.X.; writing--original draft, Y.L., H.X., and H.S.; writing--review and editing, Y.L., H.X., and H.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are openly available in [KITTI and SUN-RGBD dataset] at [doi: 10.1109/CVPR.2012.6248074 and 10.1109/CVPR.2015.7298655], reference number [8,9].  
**Acknowledgments:** This material is based on work supported by the Ministry of Trade, Industry and Energy (MOTIE, Korea) under the Industrial Technology Innovation Program (10080619).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **Bathtub** & **Bed** & **Bookshelf** & **Chair** & **Desk** & **Dresser** & **Nightstand** & **Sofa** & **Table** & **Touilet** & **Mean** \\ \hline DSS [14] & 44.2 & 78.8 & 11.9 & 61.2 & 20.5 & 6.4 & 15.4 & 53.5 & 50.3 & 78.9 & 42.1 \\ COG [29] & 58.26 & 63.67 & 31.80 & 62.17 & **45.19** & 15.47 & 27.36 & 51.02 & 51.29 & 70.07 & 47.63 \\
2 Ddriven3D [30] & 43.45 & 64.48 & 31.40 & 48.27 & 27.93 & 25.92 & 41.92 & 50.39 & 37.02 & 80.40 & 45.12 \\ PointFusion [31] & 37.26 & 68.57 & **37.69** & 55.09 & 17.16 & 23.95 & 23.33 & 53.83 & 31.03 & 83.80 & 45.38 \\ [PERSON] et al. [32] & **76.2** & 73.2 & 32.9 & 60.5 & 34.5 & 13.5 & 30.4 & 60.4 & **55.4** & 73.7 & 51.0 \\ F-PointNet [2] & 43.3 & 81.1 & 33.3 & 64.2 & 24.7 & 32.0 & 58.1 & 61.1 & 51.1 & **90.9** & 54.0 \\ Frustum ConvNet [1] & 61.32 & 83.19 & 36.46 & 64.4 & 29.67 & 35.10 & **58.42** & 66.61 & 53.34 & 86.99 & 57.55 \\ FCAM (Ours) & 57.18 & **84.86** & 36.04 & **65.32** & 32.40 & **36.51** & 57.62 & **66.91** & 54.87 & 88.36 & **58.01** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Three-dimensional (3D) object detection AP (%) on the SUN-RGBD test set (IoU 0.25). The best result are in bold.