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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2412.11883 |
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| _version_ | 1866910747735359488 |
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| author | Maquignaz, Ian J. |
| author_facet | Maquignaz, Ian J. |
| contents | Accurate environment maps are a key component in rendering photorealistic outdoor scenes with coherent illumination. They enable captivating visual arts, immersive virtual reality and a wide range of engineering and scientific applications. Recent works have extended sky-models to be more comprehensive and inclusive of cloud formations but existing approaches fall short in faithfully recreating key-characteristics in physically captured HDRI. As we demonstrate, environment maps produced by sky-models do not relight scenes with the same tones, shadows, and illumination coherence as physically captured HDR imagery. Though the visual quality of DNN-generated LDR and HDR imagery has greatly progressed in recent years, we demonstrate this progress to be tangential to sky-modelling. Due to the Extended Dynamic Range (EDR) of 14EV required for outdoor environment maps inclusive of the sun, sky-modelling extends beyond the conventional paradigm of High Dynamic Range Imagery (HDRI). In this work, we propose an all-weather sky-model, learning weathered-skies directly from physically captured HDR imagery. Per user-controlled positioning of the sun and cloud formations, our model (AllSky) allows for emulation of physically captured environment maps with improved retention of the Extended Dynamic Range (EDR) of the sky. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11883 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Physically-Based Sky-Modeling Maquignaz, Ian J. Computer Vision and Pattern Recognition Image and Video Processing Accurate environment maps are a key component in rendering photorealistic outdoor scenes with coherent illumination. They enable captivating visual arts, immersive virtual reality and a wide range of engineering and scientific applications. Recent works have extended sky-models to be more comprehensive and inclusive of cloud formations but existing approaches fall short in faithfully recreating key-characteristics in physically captured HDRI. As we demonstrate, environment maps produced by sky-models do not relight scenes with the same tones, shadows, and illumination coherence as physically captured HDR imagery. Though the visual quality of DNN-generated LDR and HDR imagery has greatly progressed in recent years, we demonstrate this progress to be tangential to sky-modelling. Due to the Extended Dynamic Range (EDR) of 14EV required for outdoor environment maps inclusive of the sun, sky-modelling extends beyond the conventional paradigm of High Dynamic Range Imagery (HDRI). In this work, we propose an all-weather sky-model, learning weathered-skies directly from physically captured HDR imagery. Per user-controlled positioning of the sun and cloud formations, our model (AllSky) allows for emulation of physically captured environment maps with improved retention of the Extended Dynamic Range (EDR) of the sky. |
| title | Towards Physically-Based Sky-Modeling |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2412.11883 |