3D View Optimization for Improving Image Aesthetics

Achieving aesthetically pleasing photography necessitates attention to multiple factors, including composition and capture conditions, which pose challenges to novices. Prior research has explored the enhancement of photo aesthetics post-capture through 2D manipulation techniques; however, these approaches offer limited search space for aesthetics. We introduce a pioneering method that employs 3D operations to simulate the conditions at the moment of capture retrospectively. Our approach extrapolates the input image and then reconstructs the 3D scene from the extrapolated image, followed by optimization to find camera parameters and image size that yield the best 3D view with enhanced aesthetics. Comparative qualitative and quantitative assessments reveal that our method surpasses traditional 2D editing techniques with superior aesthetics.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10888966
Citations
2
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
2.55
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article

3D View Optimization for Improving Image Aesthetics

Yoshihiro Kanamori, Taichi Uchida, Yuki Endo
2 citations
Visual Attention and Saliency Detection
2.55
article

3D View Optimization for Improving Image Aesthetics

Yoshihiro Kanamori, Taichi Uchida, Yuki Endo
article en
2 citations

Abstract

Achieving aesthetically pleasing photography necessitates attention to multiple factors, including composition and capture conditions, which pose challenges to novices. Prior research has explored the enhancement of photo aesthetics post-capture through 2D manipulation techniques; however, these approaches offer limited search space for aesthetics. We introduce a pioneering method that employs 3D operations to simulate the conditions at the moment of capture retrospectively. Our approach extrapolates the input image and then reconstructs the 3D scene from the extrapolated image, followed by optimization to find camera parameters and image size that yield the best 3D view with enhanced aesthetics. Comparative qualitative and quantitative assessments reveal that our method surpasses traditional 2D editing techniques with superior aesthetics.

University of Tsukuba (JP)
Openalex Percentile: Top 12%
Visual Attention and Saliency Detection
2.55
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3D View Optimization for Improving Image Aesthetics — Yoshihiro Kanamori, Taichi Uchida, et al. · (2025) | TGRS Research Map | TGRS