Point cloud object classification has gained attention from many researchers since the emergence of public dataset like ModelNet and ShapeNet, which contains full surface objects. However, in practice, objects captured using LiDAR are only partially covered in the scanned area, making such a task burdensome. Here, we proposed a solution to overcome those problems. It is a novel fuzzy convolutional inference (FuzzConv) incorporated with depthwise over-parameterization (DOConv). Instead of applying raw data, the point clouds are transformed into a 3D voxel. We utilized EfficientNet as our backbone and modified the Mobile inverted Bottleneck Convolution (MBConv) with DOConv. In the last fully connected (FC) layer, we added the FuzzConv layer as an inference before feeding the feature map to the output layer. Consequently, to validate the performance of our model, we undertake an evaluation with multiple classifications in ModelNet10, ModelNet40, and our core dataset, the point cloud of human poses. Accuracy, loss, number of parameters, loss, precision, and F1-scores are employed as performance indicators. As a result, our model achieved top performance regarding the accuracy and loss value for the primary dataset, 83 % and 0.56, for ModelNet10 88.1 % and 0.56, and ModelNet40 74.1 % and 1.15.

Original languageEnglish
Title of host publicationTENCON 2023 - 2023 IEEE Region 10 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350302196
Publication statusPublished - 2023
Event38th IEEE Region 10 Conference, TENCON 2023 - Chiang Mai, Thailand
Duration: 31 Oct 20233 Nov 2023

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference38th IEEE Region 10 Conference, TENCON 2023
CityChiang Mai


  • Fuzzy Convolution
  • Human Pose
  • Lightweight CNN
  • Point Cloud Classification
  • Voxel


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