Browsing by Author "Moon, Bochang"

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    Consistent Post-Reconstruction for Progressive Photon Mapping
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Choi, Hajin; Moon, Bochang; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
    Photon mapping is a light transport algorithm that simulates various rendering effects (e.g., caustics) robustly, and its progressive variants, progressive photon mapping (PPM) methods, can produce a biased but consistent rendering output. PPM estimates radiance using a kernel density estimation whose parameters (bandwidths) are adjusted progressively, and this refinement enables to reduce its estimation bias. Nonetheless, many iterations (and thus a large number of photons) are often required until PPM produces nearly converged estimates. This paper proposes a post-reconstruction that improves the performance of PPM by reducing residual errors in PPM estimates. Our key idea is to take multiple PPM estimates with multi-level correlation structures, and fuse the input images using a weight function trained by supervised learning with maintaining the consistency of PPM. We demonstrate that our technique boosts an existing PPM technique for various rendering scenes.
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    Feature Generation for Adaptive Gradient-Domain Path Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Back, Jonghee; Yoon, Sung-Eui; Moon, Bochang; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    In this paper, we propose a new technique to incorporate recent adaptive rendering approaches built upon local regression theory into a gradient-domain path tracing framework, in order to achieve high-quality rendering results. Our method aims to reduce random artifacts introduced by random sampling on image colors and gradients. Our high-level approach is to identify a feature image from noisy gradients, and pass the image to an existing local regression based adaptive method so that adaptive sampling and reconstruction using our feature can boost the performance of gradient-domain rendering. To fulfill our idea, we derive an ideal feature in the form of image gradients and propose an estimation process for the ideal feature in the presence of noise in image gradients. We demonstrate that our integrated adaptive solution leads to performance improvement for a gradient-domain path tracer, by seamlessly incorporating recent adaptive sampling and reconstruction strategies through our estimated feature.
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    Gradient Outlier Removal for Gradient-Domain Path Tracing
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ha, Saerom; Oh, Sojin; Back, Jonghee; Yoon, Sung-Eui; Moon, Bochang; Alliez, Pierre and Pellacini, Fabio
    We present a new outlier removal technique for a gradient-domain path tracing (G-PT) that computes image gradients as well as colors. Our approach rejects gradient outliers whose estimated errors are much higher than those of the other gradients for improving reconstruction quality for the G-PT. We formulate our outlier removal problem as a least trimmed squares optimization, which employs only a subset of gradients so that a final image can be reconstructed without including the gradient outliers. In addition, we design this outlier removal process so that the chosen subset of gradients maintains connectivity through gradients between pixels, preventing pixels from being isolated. Lastly, the optimal number of inlier gradients is estimated to minimize our reconstruction error. We have demonstrated that our reconstruction with robustly rejecting gradient outliers produces visually and numerically improved results, compared to the previous screened Poisson reconstruction that uses all the gradients.
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    Noise Reduction on G‐Buffers for Monte Carlo Filtering
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Moon, Bochang; Iglesias‐Guitian, Jose A.; McDonagh, Steven; Mitchell, Kenny; Chen, Min and Zhang, Hao (Richard)
    We propose a novel pre‐filtering method that reduces the noise introduced by depth‐of‐field and motion blur effects in geometric buffers (G‐buffers) such as texture, normal and depth images. Our pre‐filtering uses world positions and their variances to effectively remove high‐frequency noise while carefully preserving high‐frequency edges in the G‐buffers. We design a new anisotropic filter based on a per‐pixel covariance matrix of world position samples. A general error estimator, Stein's unbiased risk estimator, is then applied to estimate the optimal trade‐off between the bias and variance of pre‐filtered results. We have demonstrated that our pre‐filtering improves the results of existing filtering methods numerically and visually for challenging scenes where depth‐of‐field and motion blurring introduce a significant amount of noise in the G‐buffers.We propose a novel pre‐filtering method that reduces the noise introduced by depth‐of‐field and motion blur effects in geometric buffers (G‐buffers) such as texture, normal and depth images. Our pre‐filtering uses world positions and their variances to effectively remove high‐frequency noise while carefully preserving high‐frequency edges in the G‐buffers. We design a new anisotropic filter based on a per‐pixel covariance matrix of world position samples. A general error estimator, Stein's unbiased risk estimator, is then applied to estimate the optimal trade‐off between the bias and variance of pre‐filtered results.