Abstract
Inverse lighting is a technique for estimating the illumination distribution of a scene from a single image. Conventionally, inverse lighting assumes either a linear radiometric response function or a convex scene without cast shadows. Unfortunately, however, consumer cameras usually have unknown and nonlinear radiometric response functions, and then the existing methods do not work well for images such as Internet photos taken by using those cameras. Moreover, it is known that the high-frequency components of an illumination distribution cannot be recovered from diffuse reflection components without cast shadows. In this paper, we propose a method for jointly estimating both an illumination distribution and a radiometric response function from cast shadows. Specifically, our proposed method represents the illumination distribution and the response function by using Haar wavelets with the sparseness constraint and polynomials respectively, and then estimates their coefficients. We conducted a number of experiments by using both synthetic and real images, and confirmed that our method works better than the existing methods. In addition, we showed that masking pixels near edges makes the joint estimation robust for real images with approximate geometry.
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Notes
- 1.
The non-negativity constraint is represented by the linear constraints on the coefficients of the illumination distribution \(\alpha _n\).
- 2.
The geometry of the scene is the same as that of the real images in the next subsection.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP17H00744.
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Nakashima, T., Matsuoka, R., Okabe, T. (2020). Inverse Lighting from Cast Shadows Under Unknown Radiometric Response Function. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_27
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DOI: https://doi.org/10.1007/978-981-15-4818-5_27
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