Multiscale fusion and convolution spatial propagation networks for deep complementation of outdoor scenes

Hui Chen; Shuqi Liu; Heping Huang; Muhammad llyas Menhas

doi:10.1117/12.2680104

27 June 2023 Multiscale fusion and convolution spatial propagation networks for deep complementation of outdoor scenes

Hui Chen, Shuqi Liu, Heping Huang, Muhammad llyas Menhas

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127053C (2023) https://doi.org/10.1117/12.2680104
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

In autonomous driving and other robotics, rich depth perception is critical for 3D reconstruction tasks of outdoor landscapes. Many neural networks combine sparse depth maps with high-quality RGB images to generate a dense effect, resulting in dense depth maps. However, they frequently combine LiDAR and RGB image data by conducting feature concatenation or element addition, which results in the loss of some features as well as changes to the depth values of the original sparse data. To address these issues, this article proposes that the relationship between spatial and channel attention be used to link local and global features in order to accurately complete and correct sparse input so that the RGB images can better lead the depth completion job. We also use an affinity matrix to keep the original depth values in order to make the RGB image simply act as a guide without modifying the original pixel depth. We tested and assessed our algorithm on the KITTI dataset, and it outperformed the existing network in outdoor situations.

Citation Download Citation

Hui Chen, Shuqi Liu, Heping Huang, and Muhammad llyas Menhas "Multiscale fusion and convolution spatial propagation networks for deep complementation of outdoor scenes", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127053C (27 June 2023); https://doi.org/10.1117/12.2680104

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KEYWORDS

Depth maps

Image fusion

RGB color model

3D modeling

Convolution

Education and training

LIDAR

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