DSpace Repository :: Browsing by Author "Bao, Hujun"

Browsing by Author "Bao, Hujun"

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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.
Cosserat Rod with rh-Adaptive Discretization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wen, Jiahao; Chen, Jiong; Nobuyuki, Umetani; Bao, Hujun; Huang, Jin; Eisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lue
Rod-like one-dimensional elastic objects often exhibit complex behaviors which pose great challenges to the discretization method for pursuing a faithful simulation. By only moving a small portion of material points, the Eulerian-on-Lagrangian (EoL) method already shows great adaptivity to handle sharp contact, but it is still far from enough to reproduce rich and complex geometry details arising in simulations. In this paper, we extend the discrete configuration space by unifying all Lagrangian and EoL nodes in representation for even more adaptivity with every sample being assigned with a dynamic material coordinate. However, this great extension will immediately bring in much more redundancy in the dynamic system. Therefore, we propose additional energy to control the spatial distribution of all material points, seeking to equally space them with respect to a curvature-based density field as a monitor. This flexible approach can effectively constrain the motion of material points to resolve numerical degeneracy, while simultaneously enables them to notably slide inside the parametric domain to account for the shape parameterization. Besides, to accurately respond to sharp contact, our method can also insert or remove nodes online and adjust the energy stiffness to suppress possible jittering artifacts that could be excited in a stiff system. As a result of this hybrid rh-adaption, our proposed method is capable of reproducing many realistic rod dynamics, such as excessive bending, twisting and knotting while only using a limited number of elements.
Economic Upper Bound Estimation in Hausdorff Distance Computation for Triangle Meshes
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Zheng, Yicun; Sun, Haoran; Liu, Xinguo; Bao, Hujun; Huang, Jin; Hauser, Helwig and Alliez, Pierre
The Hausdorff distance is one of the most fundamental metrics for comparing 3D shapes. To compute the Hausdorff distance efficiently from a triangular mesh to another triangular mesh , one needs to cull the unnecessary triangles on quickly. These triangles have no chance to improve the Hausdorff distance estimation, that is the parts with local upper bound smaller than the global lower bound. The local upper bound estimation should be tight, use fast distance computation, and involve a small number of triangles in during the reduction phase for efficiency. In this paper, we propose to use point‐triangle distance, and only involve at most four triangles in in the reduction phase. Comparing with the state‐of‐the‐art proposed by Tang et al. in 2009, which uses more costly triangle‐triangle distance and may involve a large number of triangles in reduction phase, our local upper bound estimation is faster, and with only a small impact on the tightness of the bound on error estimation. Such a more economic strategy boosts the overall performance significantly. Experiments on the Thingi10K dataset show that our method can achieve several (even over 20) times speedup on average. On a few models with different placements and resolutions, we show that close placement and large difference in resolution bring big challenges to Hausdorff distance computation, and explain why our method can achieve more significant speedup on challenging cases.
Efficient and Stable Simulation of Inextensible Cosserat Rods by a Compact Representation
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhao, Chongyao; Lin, Jinkeng; Wang, Tianyu; Bao, Hujun; Huang, Jin; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Piecewise linear inextensible Cosserat rods are usually represented by Cartesian coordinates of vertices and quaternions on the segments. Such representations use excessive degrees of freedom (DOFs), and need many additional constraints, which causes unnecessary numerical difficulties and computational burden for simulation. We propose a simple yet compact representation that exactly matches the intrinsic DOFs and naturally satisfies all such constraints. Specifically, viewing a rod as a chain of rigid segments, we encode its shape as the Cartesian coordinates of its root vertex, and use axis-angle representation for the material frame on each segment. Under our representation, the Hessian of the implicit time-stepping has special non-zero patterns. Exploiting such specialties, we can solve the associated linear equations in nearly linear complexity. Furthermore, we carefully designed a preconditioner, which is proved to be always symmetric positive-definite and accelerates the PCG solver in one or two orders of magnitude compared with the widely used block-diagonal one. Compared with other technical choices including Super-Helices, a specially designed compact representation for inextensible Cosserat rods, our method achieves better performance and stability, and can simulate an inextensible Cosserat rod with hundreds of vertices and tens of collisions in real time under relatively large time steps.
Efficient Texture Parameterization Driven by Perceptual-Loss-on-Screen
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Sun, Haoran; Wang, Shiyi; Wu, Wenhai; Jin, Yao; Bao, Hujun; Huang, Jin; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Texture mapping is a ubiquitous technique to enrich the visual effect of a mesh, which represents the desired signal (e.g. diffuse color) on the mesh to a texture image discretized by pixels through a bijective parameterization. To achieve high visual quality, large number of pixels are generally required, which brings big burden in storage, memory and transmission. We propose to use a perceptual model and a rendering procedure to measure the loss coming from the discretization, then optimize a parameterization to improve the efficiency, i.e. using fewer pixels under a comparable perceptual loss. The general perceptual model and rendering procedure can be very complicated, and non-isotropic property rooted in the square shape of pixels make the problem more difficult to solve. We adopt a two-stage strategy and use the Bayesian optimization in the triangle-wise stage. With our carefully designed weighting scheme, the mesh-wise optimization can take the triangle-wise perceptual loss into consideration under a global conforming requirement. Comparing with many parameterizations manually designed, driven by interpolation error, or driven by isotropic energy, ours can use significantly fewer pixels with comparable perception loss or vise vesa.
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.
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.
Online Global Non-rigid Registration for 3D Object Reconstruction Using Consumer-level Depth Cameras
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Xu, Jiamin; Xu, Weiwei; Yang, Yin; Deng, Zhigang; Bao, Hujun; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
We investigate how to obtain high-quality 360-degree 3D reconstructions of small objects using consumer-level depth cameras. For many homeware objects such as shoes and toys with dimensions around 0.06 - 0:4 meters, their whole projections, in the hand-held scanning process, occupy fewer than 20% pixels of the camera's image. We observe that existing 3D reconstruction algorithms like KinectFusion and other similar methods often fail in such cases even under the close-range depth setting. To achieve high-quality 3D object reconstruction results at this scale, our algorithm relies on an online global non-rigid registration, where embedded deformation graph is employed to handle the drifting of camera tracking and the possible nonlinear distortion in the captured depth data. We perform an automatic target object extraction from RGBD frames to remove the unrelated depth data so that the registration algorithm can focus on minimizing the geometric and photogrammetric distances of the RGBD data of target objects. Our algorithm is implemented using CUDA for a fast non-rigid registration. The experimental results show that the proposed method can reconstruct high-quality 3D shapes of various small objects with textures.
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.

Browsing by Author "Bao, Hujun"

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