AIM 2024 Sparse Neural Rendering Challenge: Methods and Results

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

doi:10.1007/978-3-031-91856-8_2

AIM 2024 Sparse Neural Rendering Challenge: Methods and Results

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

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

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.

Original language	English
Title of host publication	Computer Vision – ECCV 2024 Workshops, Proceedings
Editors	Alessio Del Bue, Cristian Canton, Jordi Pont-Tuset, Tatiana Tommasi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	18-35
Number of pages	18
ISBN (Print)	9783031918551
DOIs	https://doi.org/10.1007/978-3-031-91856-8_2
Publication status	Published - May 2025
Event	Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024

Publication series

Name	Lecture Notes in Computer Science
Volume	15632 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024
Country/Territory	Italy
City	Milan
Period	29/09/24 → 4/10/24

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

More information

10.1007/978-3-031-91856-8_2

Cite this

Nazarczuk, M., Catley-Chandar, S., Tanay, T., Shaw, R., Pérez-Pellitero, E., Timofte, R., Yan, X., Wang, P., Guo, Y., Wu, Y., Cai, Y., Yang, Y., Li, J., Zhou, Y., Mok, P. Y., He, Z., Xiao, Z., Chan, K. C., Lebeta Goshu, H., ... Purohit, K. (2025). AIM 2024 Sparse Neural Rendering Challenge: Methods and Results. In A. Del Bue, C. Canton, J. Pont-Tuset, & T. Tommasi (Eds.), Computer Vision – ECCV 2024 Workshops, Proceedings (pp. 18-35). (Lecture Notes in Computer Science; Vol. 15632 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-91856-8_2

Nazarczuk, Michal ; Catley-Chandar, Sibi ; Tanay, Thomas et al. / AIM 2024 Sparse Neural Rendering Challenge: Methods and Results. Computer Vision – ECCV 2024 Workshops, Proceedings. editor / Alessio Del Bue ; Cristian Canton ; Jordi Pont-Tuset ; Tatiana Tommasi. Springer Science and Business Media Deutschland GmbH, 2025. pp. 18-35 (Lecture Notes in Computer Science).

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abstract = "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.",

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

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",

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Nazarczuk, M, Catley-Chandar, S, Tanay, T, Shaw, R, Pérez-Pellitero, E, Timofte, R, Yan, X, Wang, P, Guo, Y, Wu, Y, Cai, Y, Yang, Y, Li, J, Zhou, Y, Mok, PY, He, Z , Xiao, Z, Chan, KC, Lebeta Goshu, H, Yang, C, Dong, R, Xiao, J, Lam, KM, Hao, J, Gao, Q, Zu, Y, Zhang, J, Jiao, L, Liu, X & Purohit, K 2025, AIM 2024 Sparse Neural Rendering Challenge: Methods and Results. in A Del Bue, C Canton, J Pont-Tuset & T Tommasi (eds), Computer Vision – ECCV 2024 Workshops, Proceedings. Lecture Notes in Computer Science, vol. 15632 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 18-35, Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024, Milan, Italy, 29/09/24. https://doi.org/10.1007/978-3-031-91856-8_2

AIM 2024 Sparse Neural Rendering Challenge: Methods and Results. / Nazarczuk, Michal; Catley-Chandar, Sibi; Tanay, Thomas et al.
Computer Vision – ECCV 2024 Workshops, Proceedings. ed. / Alessio Del Bue; Cristian Canton; Jordi Pont-Tuset; Tatiana Tommasi. Springer Science and Business Media Deutschland GmbH, 2025. p. 18-35 (Lecture Notes in Computer Science; Vol. 15632 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Xiao, Zhe

AU - Chan, Kin Chung

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AU - Yang, Cuixin

AU - Dong, Rongkang

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AU - Lam, Kin Man

AU - Hao, Jiayao

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Nazarczuk M, Catley-Chandar S, Tanay T, Shaw R, Pérez-Pellitero E, Timofte R et al. AIM 2024 Sparse Neural Rendering Challenge: Methods and Results. In Del Bue A, Canton C, Pont-Tuset J, Tommasi T, editors, Computer Vision – ECCV 2024 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2025. p. 18-35. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-91856-8_2

AIM 2024 Sparse Neural Rendering Challenge: Methods and Results

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