Most existing 3D object classification and retrieval algorithms rely on one-off supervised
learning on closed 3D object sets and tend to provide rigid convolutional neural networks …

The rapid development of 3D technique has led to the dramatic increase in 3D data. The
scalable and effective 3D object retrieval and classification algorithms become mandatory …

Most existing 3D object recognition algorithms focus on leveraging the strong discriminative
power of deep learning models with softmax loss for the classification of 3D data, while …

Abstract 3D object classification has attracted appealing attentions in academic researches
and industrial applications. However, most existing methods need to access the training …

With the rapid development and wide application of computer, camera device, network and
hardware technology, 3D object (or model) retrieval has attracted widespread attention and …

In recent years, research interest in object retrieval has shifted from 2D towards 3D data.
Despite many well-designed approaches, we point out that limitations still exist and there is …

Learning robust and discriminative representations is essential for 3D object retrieval. In this
paper, we present an improved Multi-view Convolutional Neural Network (MVCNN) for view …

Foundation models, such as ULIP-2 (Xue et al., 2023) recently projected forward the field of
3D deep learning. These models are trained with significantly more data and show superior …

View-based methods have achieved state-of-the-art performance in 3D object retrieval.
However, view-based methods still encounter two major challenges. The first is how to …

Recent advances in 3D modeling software and 3D capture devices contribute to the
availability of large-scale 3D objects. However, manually labelled large-scale 3D object …