AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929510867271680 |
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| author | Nazarczuk, Michal Catley-Chandar, Sibi Tanay, Thomas Shaw, Richard Pérez-Pellitero, Eduardo Timofte, Radu Yan, Xing Wang, Pan Guo, Yali Wu, Yongxin Cai, Youcheng Yang, Yanan Li, Junting Zhou, Yanghong Mok, P. Y. He, Zongqi Xiao, Zhe Chan, Kin-Chung Goshu, Hana Lebeta Yang, Cuixin Dong, Rongkang Xiao, Jun Lam, Kin-Man Hao, Jiayao Gao, Qiong Zu, Yanyan Zhang, Junpei Jiao, Licheng Liu, Xu Purohit, Kuldeep |
| author_facet | Nazarczuk, Michal Catley-Chandar, Sibi Tanay, Thomas Shaw, Richard Pérez-Pellitero, Eduardo Timofte, Radu Yan, Xing Wang, Pan Guo, Yali Wu, Yongxin Cai, Youcheng Yang, Yanan Li, Junting Zhou, Yanghong Mok, P. Y. He, Zongqi Xiao, Zhe Chan, Kin-Chung Goshu, Hana Lebeta Yang, Cuixin Dong, Rongkang Xiao, Jun Lam, Kin-Man Hao, Jiayao Gao, Qiong Zu, Yanyan Zhang, Junpei Jiao, Licheng Liu, Xu Purohit, Kuldeep |
| contents | This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their respective results. The challenge aims at producing novel camera view synthesis of diverse scenes from sparse image observations. It is composed of two tracks, with differing levels of sparsity; 3 views in Track 1 (very sparse) and 9 views in Track 2 (sparse). Participants are asked to optimise objective fidelity to the ground-truth images as measured via the Peak Signal-to-Noise Ratio (PSNR) metric. For both tracks, we use the newly introduced Sparse Rendering (SpaRe) dataset and the popular DTU MVS dataset. In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2. The submitted models are varied and push the boundaries of the current state-of-the-art in sparse neural rendering. A detailed description of all models developed in the challenge is provided in this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15045 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIM 2024 Sparse Neural Rendering Challenge: Methods and Results Nazarczuk, Michal Catley-Chandar, Sibi Tanay, Thomas Shaw, Richard Pérez-Pellitero, Eduardo Timofte, Radu Yan, Xing Wang, Pan Guo, Yali Wu, Yongxin Cai, Youcheng Yang, Yanan Li, Junting Zhou, Yanghong Mok, P. Y. He, Zongqi Xiao, Zhe Chan, Kin-Chung Goshu, Hana Lebeta Yang, Cuixin Dong, Rongkang Xiao, Jun Lam, Kin-Man Hao, Jiayao Gao, Qiong Zu, Yanyan Zhang, Junpei Jiao, Licheng Liu, Xu Purohit, Kuldeep Computer Vision and Pattern Recognition This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript focuses on the competition set-up, the proposed methods and their respective results. The challenge aims at producing novel camera view synthesis of diverse scenes from sparse image observations. It is composed of two tracks, with differing levels of sparsity; 3 views in Track 1 (very sparse) and 9 views in Track 2 (sparse). Participants are asked to optimise objective fidelity to the ground-truth images as measured via the Peak Signal-to-Noise Ratio (PSNR) metric. For both tracks, we use the newly introduced Sparse Rendering (SpaRe) dataset and the popular DTU MVS dataset. In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2. The submitted models are varied and push the boundaries of the current state-of-the-art in sparse neural rendering. A detailed description of all models developed in the challenge is provided in this paper. |
| title | AIM 2024 Sparse Neural Rendering Challenge: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.15045 |