Tone-Mapped Mean-Shift Based Environment Map Sampling

Wei Feng       Ying Yang        Liang Wan        Changguo Yu

IEEE Transactions on Visualization and Computer Graphics,

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.

Paper

(PDF, 4.06M)

BibTex:

@article{Feng2016ToneMappedMB,
    title    ={Tone-Mapped Mean-Shift Based Environment Map                 Sampling},
    author   ={Wei Feng and Ying Yang and Liang Wan                  and Changguo Yu},
    journal  ={IEEE Transactions on Visualization and                  Computer Graphics},
    year     ={2016},
    volume   ={22},
    number   ={8},
    pages    ={2187-2199}
}