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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 [22] and the popular DTU MVS dataset [1]. 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.

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AIM 2024 Sparse Neural Rendering Challenge: Methods and Results

  • 会议论文

  • 发表日期：

    2025年

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  • Michal Nazarczuk,
  • Sibi Catley-Chandar,
  • Thomas Tanay,
  • Richard Shaw,
  • Eduardo Pérez-Pellitero,
  • Radu Timofte,
  • Xing Yan,
  • Pan Wang,
  • Yali Guo,
  • Yongxin Wu,
  • Youcheng Cai,
  • Yanan Yang,
  • Junting Li,
  • Yanghong Zhou,
  • P. Y. Mok,
  • Zongqi He,
  • Zhe Xiao,
  • Kin-Chung Chan,
  • Hana Lebeta Goshu,
  • Cuixin Yang,
  • Rongkang Dong,
  • Jun Xiao,
  • Kin-Man Lam,
  • Jiayao Hao,
  • Qiong Gao,
  • Yanyan Zu,
  • Junpei Zhang,
  • Licheng Jiao,
  • Xu Liu,
  • Kuldeep Purohit

摘要

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 [22] and the popular DTU MVS dataset [1]. 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.