Prof. Xinglin Liu Profile

Prof. Xinglin Liu

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Proceedings Article | 27 June 2023 Paper

Multi-scale transformer 3D plane recovery

Fei Ren, Qingling Chang, Xinglin Liu, Yan Cui

Proceedings Volume 12705, 1270529 (2023) https://doi.org/10.1117/12.2680197

KEYWORDS: Transformers, Image segmentation, Feature extraction, Surface plasmons, Machine learning, Image restoration, 3D image reconstruction, Education and training, RGB color model, Object detection

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The 3D plane recovery and reconstruction method of a single image aim to identify plane instance regions and estimate plane parameters and is widely used in technologies such as AR/VR(Augmented Reality/Virtual Reality). In recent years, significant progress has been achieved in single-image 3D plane recovery approaches based on deep learning, however much of the research has focused on overall plane segmentation performance rather than the accuracy of small-scale plane segmentation. Given the lack of an accurate definition of the segmentation of small-scale plane regions in existing methods, we propose a novel multi-scale transformer-based plane recognition and recovery model, which can accurately identify the edges of small-scale plane regions. The network branches composed of the model are used to detect plane and non-planar areas respectively. Different input feature scales make the two network branches have different global feature extraction capabilities. Finally, the two branches are strengthened to recognize plane areas in the same scene through mutual information loss. The consistency makes the two branch networks have the ability to share parameters. The experimental results in the Scannet and NYU V2 datasets show that the model can accurately identify small-sized plane areas, and the detection and recovery accuracy reaches the state-of-the-art effect.

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