Browsing by Author "Wang, Rui"

Now showing 1 - 6 of 6
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    Automatic Band-Limited Approximation of Shaders Using Mean-Variance Statistics in Clamped Domain
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Li, Shi; Wang, Rui; Huo, Yuchi; Zheng, Wenting; Hua, Wei; Bao, Hujun; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
    In this paper, we present a new shader smoothing method to improve the quality and generality of band-limiting shader programs. Previous work [YB18] treats intermediate values in the program as random variables, and utilizes mean and variance statistics to smooth shader programs. In this work, we extend such a band-limiting framework by exploring the observation that one intermediate value in the program is usually computed by a complex composition of functions, where the domain and range of composited functions heavily impact the statistics of smoothed programs. Accordingly, we propose three new shader smoothing rules for specific composition of functions by considering the domain and range, enabling better mean and variance statistics of approximations. Aside from continuous functions, the texture, such as color texture or normal map, is treated as a discrete function with limited domain and range, thereby can be processed similarly in the newly proposed framework. Experiments show that compared with previous work, our method is capable of generating better smoothness of shader programs as well as handling a broader set of shader programs.
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    MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Ren, Haocheng; Zhang, Hao; Zheng, Jia; Zheng, Jiaxiang; Tang, Rui; Huo, Yuchi; Bao, Hujun; Wang, Rui; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain-Specific Language, allowing users to select scenes from the commercial indoor scene database, synthesize scenes for different tasks with customized rules, and render various types of imagery data, such as color images, geometric structures, semantic labels. Our system eases the difficulty of customizing massive scenes for different tasks and relieves users from manipulating fine-grained scene configurations by providing user-controllable randomness using multilevel samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks. The project page is at https://coohom.github.io/MINERVAS.
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    Multirate Shading with Piecewise Interpolatory Approximation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Hu, Yiwei; Yuan, Yazhen; Wang, Rui; Yang, Zhuo; Bao, Hujun; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    Evaluating shading functions on geometry surfaces dominates the rendering computation. A high-quality but time-consuming estimate is usually achieved with a dense sampling rate for pixels or sub-pixels. In this paper, we leverage sparsely sampled points on vertices of dynamically-generated subdivision surfaces to approximate the ground-truth shading signal by piecewise linear reconstruction. To control the introduced interpolation error at runtime, we analytically derive an L∞ error bound and compute the optimal subdivision surfaces based on a user-specified error threshold. We apply our analysis on multiple shading functions including Lambertian, Blinn-Phong, Microfacet BRDF and also extend it to handle textures, yielding easy-to-compute formulas. To validate our derivation, we design a forward multirate shading algorithm powered by hardware tessellator that moves shading computation at pixels to the vertices of subdivision triangles on the fly. We show our approach significantly reduces the sampling rates on various test cases, reaching a speedup ratio of 134% ∼ 283% compared to dense per-pixel shading in current graphics hardware.
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    PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sharma, Gopal; Dash, Bidya; RoyChowdhury, Aruni; Gadelha, Matheus; Loizou, Marios; Cao, Liangliang; Wang, Rui; Learned-Miller, Erik G.; Maji, Subhransu; Kalogerakis, Evangelos; Campen, Marcel; Spagnuolo, Michela
    We present PRIFIT, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks. PRIFIT combines geometric primitive fitting with point-based representation learning. Its key idea is to learn point representations whose clustering reveals shape regions that can be approximated well by basic geometric primitives, such as cuboids and ellipsoids. The learned point representations can then be re-used in existing network architectures for 3D point cloud segmentation, and improves their performance in the few-shot setting. According to our experiments on the widely used ShapeNet and PartNet benchmarks, PRIFIT outperforms several state-of-the-art methods in this setting, suggesting that decomposability into primitives is a useful prior for learning representations predictive of semantic parts. We present a number of ablative experiments varying the choice of geometric primitives and downstream tasks to demonstrate the effectiveness of the method.
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    Real-time Monte Carlo Denoising with Weight Sharing Kernel Prediction Network
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Fan, Hangming; Wang, Rui; Huo, Yuchi; Bao, Hujun; Bousseau, Adrien and McGuire, Morgan
    Real-time Monte Carlo denoising aims at removing severe noise under low samples per pixel (spp) in a strict time budget. Recently, kernel-prediction methods use a neural network to predict each pixel's filtering kernel and have shown a great potential to remove Monte Carlo noise. However, the heavy computation overhead blocks these methods from real-time applications. This paper expands the kernel-prediction method and proposes a novel approach to denoise very low spp (e.g., 1-spp) Monte Carlo path traced images at real-time frame rates. Instead of using the neural network to directly predict the kernel map, i.e., the complete weights of each per-pixel filtering kernel, we predict an encoding of the kernel map, followed by a high-efficiency decoder with unfolding operations for a high-quality reconstruction of the filtering kernels . The kernel map encoding yields a compact single-channel representation of the kernel map, which can significantly reduce the kernel-prediction network's throughput. In addition, we adopt a scalable kernel fusion module to improve denoising quality. The proposed approach preserves kernel prediction methods' denoising quality while roughly halving its denoising time for 1-spp noisy inputs. In addition, compared with the recent neural bilateral grid-based real-time denoiser, our approach benefits from the high parallelism of kernel-based reconstruction and produces better denoising results at equal time.
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    Skeleton-based Generalized Cylinder Deformation under the Relative Curvature Condition
    (The Eurographics Association, 2018) Ma, Ruibin; Zhao, Qingyu; Wang, Rui; Damon, James; Rosenman, Julian; Pizer, Stephen; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Deformation of a generalized cylinder with a parameterized shape change of its centerline is a non-trivial task when the surface is represented as a high-resolution triangle mesh, particularly when self-intersection and local distortion are to be avoided. We introduce a deformation approach that satisfies these properties based on the skeleton (densely sampled centerline and cross sections) of a generalized cylinder. Our approach uses the relative curvature condition to extract a reasonable centerline for a generalized cylinder whose orthogonal cross sections will not intersect. Given the desired centerline shape as a parametric curve, the displacements on the cross sections are determined while controlling for twisting effects, and under this constraint a vertex-wise displacement field is calculated by minimizing a quadratic surface bending energy. The method is tested on complicated generalized cylindrical objects. In particular, we discuss one application of the method for human colon (large intestine) visualization.