Work

A Two-Stage Framework for Rotation-Robust 3D Object Classification

Year: 2024

Type: conference-paper

Abstract: In this work, we aim at developing a rotation-robust framework for 3D object classification. To this end, we propose a novel two-stage framework that utilizes multi-view images to represent 3D objects. The first stage focuses on precise feature extraction from individual images. The second stage performs multi-view feature fusion and classification through a novel attention module and a two-layer pooling operation, which significantly enhance the model's robustness to rotation of objects. Experimental results on the ModelNet40-rotated dataset demonstrate that the proposed method achieves state-of-the-art performance, with significant improvements in instance accuracy and weighted class accuracy under various rotation settings, outperforming existing view-based methods.

Source: International Conference on Electronic Information Technology and Computer Engineering (EITCE)

DOI: 10.1145/3711129.3711255

Authors: Fengming Ji, Jinglan Tian, Huixuan Wang, Yan Zhang, Jingliang Peng

Institutions: University of Jinan, Shandong University, Shandong Jianzhu University

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4407775724

FWCI:

Cites: 21

Cited by:

Related to: 10