Browsing by Author "Huo, Yuchi"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.