Point Cloud-Based 3D Object Classification With Non Local Attention and Lightweight Convolution Neural Networks

Abstract

Three-dimensional (3D) object classification is crucial in various applications, including autonomous driving, robotics, and augmented reality, where precise detection of objects in 3D space is required. Traditional techniques for 3D object classification encompass voxel-based representations, multi-view projections, and point cloud analysis. However, these approaches can be computationally intensive, and might not capture complete structure of the underlying object. This research introduces a novel lightweight model for 3D object classification based on point clouds that achieves higher accuracy while requiring minimal computing overhead. The proposed system incorporates adaptive sampling, an Attentive-Convolution Feature block, a Dualpath feature processing block, and a non-local attention mechanism to enhance feature representation and classification performance. To our knowledge, this is the first study to propose a lightweight TnetLight module as a transformation network that aligns and converts input point clouds, capturing subtle geometric variations and highlighting differences between objects to enhance shape recognition and differentiation accuracy. Additionally, the model integrates an Attentive-Convolution Feature extraction block that combines local geometric features with global contextual information, enhancing the network’s ability to capture both detailed and broader characteristics for better differentiation between objects with slight variations. The network was evaluated on the ModelNet10 dataset, achieving an accuracy of 94.4%.

Keywords

• 3D object classification
• lightweight CNN
• point cloud
• non-local attention
• dualpath block
• adaptive sampling