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Feng W, Yang Y, Wan L, et al. Tone-Mapped Mean-Shift Based Environment Map Sampling. IEEE Trans Vis Comput Graph. 2015;22(9):2187-99doi: 10.1109/TVCG.2015.2500236.
Feng, W., Yang, Y., Wan, L., & Yu, C. (2016). Tone-Mapped Mean-Shift Based Environment Map Sampling. IEEE transactions on visualization and computer graphics, 22(9), 2187-99. https://doi.org/10.1109/TVCG.2015.2500236
Feng, Wei, et al. "Tone-Mapped Mean-Shift Based Environment Map Sampling." IEEE transactions on visualization and computer graphics vol. 22,9 (2016): 2187-99. doi: https://doi.org/10.1109/TVCG.2015.2500236
Feng W, Yang Y, Wan L, Yu C. Tone-Mapped Mean-Shift Based Environment Map Sampling. IEEE Trans Vis Comput Graph. 2016 Sep;22(9):2187-99. doi: 10.1109/TVCG.2015.2500236. Epub 2015 Nov 12. PMID: 26584494.
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IEEE Trans Vis Comput Graph. 2016 Sep;22(9):2187-99. doi: 10.1109/TVCG.2015.2500236. Epub 2015 Nov 12.

Tone-Mapped Mean-Shift Based Environment Map Sampling.

IEEE transactions on visualization and computer graphics

Wei Feng, Ying Yang, Liang Wan, Changguo Yu

PMID: 26584494 DOI: 10.1109/TVCG.2015.2500236

Abstract

In this paper, we present a novel approach for environment map sampling, which is an effective and pragmatic technique to reduce the computational cost of realistic rendering and get plausible rendering images. The proposed approach exploits the advantage of adaptive mean-shift image clustering with aid of tone-mapping, yielding oversegmented strata that have uniform intensities and capture shapes of light regions. The resulted strata, however, have unbalanced importance metric values for rendering, and the strata number is not user-controlled. To handle these issues, we develop an adaptive split-and-merge scheme that refines the strata and obtains a better balanced strata distribution. Compared to the state-of-the-art methods, our approach achieves comparable and even better rendering quality in terms of SSIM, RMSE and HDRVDP2 image quality metrics. Experimental results further show that our approach is more robust to the variation of viewpoint, environment rotation, and sample number.

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