Work

MFFNet: Multimodal feature fusion network for RGB-D transparent object detection

Year: 2024

Type: article

Abstract: Transparent objects are ubiquitous in everyday life, but how to detect them is full of challenges. Transparent objects hardly reflect light, and they usually transmit the appearance of their surroundings, making it difficult to distinguish them from their surroundings. Existing methods usually use only RGB (Red Green Blue) images as input, ignoring the role of depth maps in transparent object detection. In this article, we try to improve the detection performance of transparent objects by fusing RGB and depth information. Specifically, we propose a multimodal fusion network that fuses RGB and depth modalities in a complementary way. Moreover, extensive experiments and ablation studies on the RGB-D (RGB-Depth) transparent object dataset demonstrate the excellent performance of our method.

Source: International Journal of Advanced Robotic Systems

DOI: 10.1177/17298806241283373

Authors: Li Zhu, Tuanjie Li, Yuming Ning, Yan Zhang

Institution: Xidian University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,000

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities