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Depth Map Super-Resolution Reconstruction Based on Hybrid Multimodal Attention Fusion Correction
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Bing Hou1), Xiucheng Dong1, 2), *, Chencheng Yang1), Xiao Yong1), Yaling Ju1)
Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12) : 2213 - 2224
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Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12): 2213-2224
Depth Map Super-Resolution Reconstruction Based on Hybrid Multimodal Attention Fusion Correction
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Bing Hou1), Xiucheng Dong1, 2), *, Chencheng Yang1), Xiao Yong1), Yaling Ju1)
Affiliations
  • 1)School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039
  • 2)College of Jinjiang, Sichuan University, Meishan 620860
Published: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00105
Outline
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To solve the problems of blurring and low resolution in depth maps captured by low-cost depth cameras, a hybrid multimode attention fusion correction network is proposed by combining high-resolution color images in the same scene. To effectively leverage the structure information of color images, the attention mechanism is employed to iteratively align and integrate depth features with color structural features, and the fused features are corrected through the incorporation of color structure features and edge aware maps. Initially, the deep edge aware block combines low-resolution depth maps with color edge maps to extract high-resolution depth edge maps. Then, the side-window edge preserve block is utilized to extract useful structural information from the color image, reducing the impact of extraneous texture information. Finally, multimodal attention fusion correction block utilizes attention mechanisms to extract the structural attention distribution of both depth features and color structural features, aligning and fusing these modalities. The spatial attention weights of the color structural features and high-resolution depth edge maps are used to enhance the supervision of the depth edges, reducing blurring and artifacts. Experimental results on the NYU v2 dataset and Middlebury dataset (2005) show that the average root mean square error test results are 2.96 and 1.45, respectively, which are reduced by 0.10 and 0.08 compared to suboptimal methods. The network can reconstruct high-resolution depth maps with clearer edges and fewer artifacts.

depth map super-resolution  /  attention mechanism  /  side-window convolution  /  deformable convolution
侯兵, 董秀成, 杨陈成, 雍萧, 雎雅玲. 基于混合多模注意力融合矫正的深度图超分辨率重建. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2213 -2224 . DOI: 10.3724/SP.J.1089.2024-00105
Bing Hou, Xiucheng Dong, Chencheng Yang, Xiao Yong, Yaling Ju. Depth Map Super-Resolution Reconstruction Based on Hybrid Multimodal Attention Fusion Correction[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2213 -2224 . DOI: 10.3724/SP.J.1089.2024-00105
Year 2025 volume 37 Issue 12
69
13
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Article Info
doi: 10.3724/SP.J.1089.2024-00105
  • Receive Date:2024-02-28
  • Online Date:2026-10-10
  • Published:2025-12-20
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  • Received:2024-02-28
  • Revised:2024-06-20
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Affiliations
    1)School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039
    2)College of Jinjiang, Sichuan University, Meishan 620860
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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