{"type":"ScholarlyArticle","title":"3D Surface Super-resolution from Enhanced 2D Normal Images: A Multimodal-driven Variational AutoEncoder Approach","authors":[{"name":"Wuyuan Xie","slug":"wuyuan-xie","given":"Wuyuan","family":"Xie","affiliation":"Shenzhen University , Guangdong Key Laboratory of Intelligent Information Processing"},{"name":"Tengcong Huang","slug":"tengcong-huang","given":"Tengcong","family":"Huang","affiliation":"Shenzhen University , Guangdong Key Laboratory of Intelligent Information Processing"},{"name":"Miaohui Wang","slug":"miaohui-wang","given":"Miaohui","family":"Wang","affiliation":"Shenzhen University , Guangdong Key Laboratory of Intelligent Information Processing; State Key Laboratory of Radio Frequency Heterogeneous Integration ( Shenzhen University ); Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)"}],"abstract":"3D surface super-resolution is an important technical tool in virtual reality, and it is also a research hotspot in computer vision. Due to the unstructured and irregular nature of 3D object data, it is usually difficult to obtain high-quality surface details and geometry textures via a low-cost hardware setup. In this paper, we establish a multimodal-driven variational autoencoder (mmVAE) framework to perform 3D surface enhancement based on 2D normal images. To fully leverage the multimodal learning, we investigate a multimodal Gaussian mixture model (mmGMM) to align and fuse the latent feature representations from different modalities, and further propose a cross-scale encoder-decoder structure to reconstruct high-resolution normal images. Experimental results on several benchmark datasets demonstrate that our method delivers promising surface geometry structures and details in comparison with competitive advances.","doi":"10.24963/ijcai.2023/175","url":"https://ijcai.org/proceedings/2023/175","pdf":"https://ijcai.org/proceedings/2023/0175.pdf","markdown":"https://ijcai.org/proceedings/2023/175.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/175","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":175,"pages":{"first":1578,"last":1586},"keywords":["Computer Vision: CV: 3D computer vision","Computer Vision: CV: Applications"],"videos":[],"references":[{"position":1,"raw":"Scale Type Algorithm DiLiGenT Gourd & Apple WPS PSNR↑/SSIM↑/MAE↓/MRDE↓ PSNR↑/SSIM↑/MAE↓/MRDE↓ PSNR↑/SSIM↑/MAE↓/MRDE↓ ×2 Points PU-GCN [Qian et al., 2021] 22.7884/0.8019/10.0552/9.3777 24.1669/0.7876/7.2156/1.5350 23.9856/0.7251/7.5096/5.8443 Mesh C-C [Loop and Schaefer, 2008] 24.5128/0.9105/5.0687/6.3240 26.5517/0.8771/0.8916/0.8736 26.4602/0.7999/4.0879/3.4845 Depth MSG [Voynov et al., 2019] 28.7298/0.9554/4.3926/5.9665 33.2658/0.9890/0.6083/0.6457 34.0693/0.9639/2.5894/2.4289 SISR IPT [Chen et al., 2021a] 28.7556/0.9625/3.8207/5.8153 33.7279/0.9873/1.4558/0.5557 36.7309/0.9775/2.0263/2.1598 RCAN [Zhang et al., 2018a] 30.7655/0.9759/2.8982/3.8772 34.5141/0.9899/1.2404/0.5130 38.3461/0.9782/2.1502/1.6714 MISR 3DASR [Li et al., 2019] 29.6974/0.9664/3.4351/5.1201 34.4950/0.9891/1.2877/0.5387 37.9963/0.9786/2.2238/1.7798 MNSRNet [Xie et al., 2022] 30.7720/0.9763/2.8853/4.4479 34.1527/0.9896/1.2996/0.5769 37.9990/0.9789/2.0050/1.7727 RefVAE [Deng et al., 2021] 30.5138/0.9730/3.0885/4.0017 34.5274/0.9897/1.2235/0.4702 38.4997/0.9780/2.0230/1.6969 Ours 30.7969/0.9766/2.3334/3.7977 34.5723/0.9902/1.1634/0.4476 38.5276/0.9793/2.0231/1.6326","title":"Scale Type Algorithm DiLiGenT Gourd & Apple WPS PSNR↑/SSIM↑/MAE↓/MRDE↓ PSNR↑/SSIM↑/MAE↓/MRDE↓ PSNR↑/SSIM↑/MAE↓/MRDE↓ ×2 Points PU-GCN","authors":"Qian","year":"2008","venue":null,"doi":"22.7884/0.8019/10.0552/9.377724.1669/0.7876/7.2156/1.535023.9856/0.7251/7.5096/5.8443meshc-c","url":null,"internal_url":null},{"position":2,"raw":"×4 Points PU-GCN [Qian et al., 2021] 18.5747/0.7382/13.2567/10.3674 22.1672/0.7611/9.3709/2.5241 21.7603/0.6727/11.6703/6.0484 Mesh C-C [Loop and Schaefer, 2008] 24.1655/0.8585/8.6984/7.4588 26.9087/0.8777/2.9155/1.1320 24.1063/0.7967/5.8517/3.7967 Depth MSG [Voynov et al., 2019] 24.8738/0.8937/7.8564/7.3132 29.1214/0.9705/2.1031/1.0129 29.9963/0.9185/4.2786/3.4201 SISR IPT [Chen et al., 2021a] 25.2410/0.9136/6.5397/6.6953 30.6276/0.9695/2.7039/0.9519 31.8643/0.9358/3.9953/2.5922 RCAN [Zhang et al., 2018a] 27.1337/0.9304/6.1913/4.7991 32.8232/0.9808/1.8830/0.9292 32.5700/0.9441/3.6573/2.5588 MISR 3DASR [Li et al., 2019] 26.6126/0.9176/7.0156/5.8078 32.0332/0.9723/2.5102/0.8520 31.7228/0.9322/4.1421/2.7914 MNSRNet [Xie et al., 2022] 27.3644/0.9320/5.9156/4.2861 33.0666/0.9814/1.8183/0.7351 32.6350/0.9426/3.7705/2.0440 RefVAE [Deng et al., 2021] 27.2967/0.9323/5.8756/4.5097 33.3056/0.9826/1.7447/0.7752 32.6443/0.9446/3.6538/2.4731 Ours 27.6255/0.9342/5.7964/4.2827 33.4820/0.9833/1.6881/0.6052 33.1782/0.9482/3.5907/2.1511 References","title":"×4 Points PU-GCN","authors":"Qian","year":"2008","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Alldrin et al., 2008] Neil Alldrin, Todd Zickler, and David Kriegman. 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