AdaBoost-Based 3D Object Classification from Surface and Depth Map Descriptors

In this work, we aim to advance the traditional approach to 3D object classification for its lighter computation and memory costs than the deep learning based approach. Specifically, we propose a novel algorithm that uses multiple handcrafted descriptors to characterize the 3D surface and its multi-view 2D projections, pairs each descriptor with a base classifier and applies AdaBoost to derive the final classifier. In the proposed algorithm, simple yet effective descriptors are selected or designed for lightweight construction, and shape analyses are made on multiple scales of the 2D projections and integrated for the final classification. Experiments show that the proposed algorithm achieves significantly higher accuracy and efficiency than the benchmark traditional methods. Experiments also show that, compared with the state-of-the-art benchmark deep learning based methods, the proposed algorithm wins sharply in efficiency though sacrificing the accuracy by several percent.