41-Issue 7

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https://diglib.eg.org/handle/10.2312/2633207

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Point-augmented Bi-cubic Subdivision Surfaces
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Karciauskas, Kestutis; Peters, Jorg; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Point-Augmented Subdivision (PAS) replaces complex geometry-dependent guided subdivision, known to yield high-quality surfaces, by explicit subdivision formulas that yield similarly-good limit surfaces and are easy to implement using any subdivision infrastructure: map the control net d augmented by a fixed central limit point C, to a finer net (˜d;C) = M(d;C), where the subdivision matrix M is assembled from the provided stencil Tables. Point-augmented bi-cubic subdivision improves the state of the art so that bi-cubic subdivision surfaces can be used in high-end geometric design: the highlight line distribution for challenging configurations lacks the shape artifacts usually associated with explicit iterative generalized subdivision operators near extraordinary points. Five explicit formulas define Point-augmented bi-cubic subdivision in addition to uniform B-spline knot insertion. Point-augmented bi-cubic subdivision comes in two flavors, either generating a sequence of C2-joined surface rings (PAS2) or C1-joined rings (PAS1) that have fewer pieces.
MeshFormer: High-resolution Mesh Segmentation with Graph Transformer
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Li, Yuan; He, Xiangyang; Jiang, Yankai; Liu, Huan; Tao, Yubo; Hai, Lin; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Graph transformer has achieved remarkable success in graph-based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high-resolution meshes. The main challenges are the large data size, the massive model size, and the insufficient extraction of high-resolution semantic meanings. The large data or model size necessitates unacceptably extensive computational resources, and the insufficient semantic meanings lead to inaccurate segmentation results. MeshFormer addresses these three challenges with three components. First, a boundary-preserving simplification is introduced to reduce the data size while maintaining the critical high-resolution information in segmentation boundaries. Second, a Ricci flow-based clustering algorithm is presented for constructing hierarchical structures of meshes, replacing many convolutions layers for global support with only a few convolutions in hierarchy structures. In this way, the model size can be reduced to an acceptable range. Third, we design a graph transformer with cross-resolution convolutions, which extracts richer high-resolution semantic meanings and improves segmentation results over previous methods. Experiments show that MeshFormer achieves gains from 1.0% to 5.8% on artificial and real-world datasets.
Real-Time Rendering of Eclipses without Incorporation of Atmospheric Effects
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Schneegans, Simon; Gilg, Jonas; Ahlers, Volker; Gerndt, Andreas; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
In this paper, we present a novel approach for real-time rendering of soft eclipse shadows cast by spherical, atmosphereless bodies. While this problem may seem simple at first, it is complicated by several factors. First, the extreme scale differences and huge mutual distances of the involved celestial bodies cause rendering artifacts in practice. Second, the surface of the Sun does not emit light evenly in all directions (an effect which is known as limb darkening). This makes it impossible to model the Sun as a uniform spherical light source. Finally, our intended applications include real-time rendering of solar eclipses in virtual reality, which require very high frame rates. As a solution to these problems, we precompute the amount of shadowing into an eclipse shadow map, which is parametrized so that it is independent of the position and size of the occluder. Hence, a single shadow map can be used for all spherical occluders in the Solar System. We assess the errors introduced by various simplifications and compare multiple approaches in terms of performance and precision. Last but not least, we compare our approaches to the state-of-the-art and to reference images. The implementation has been published under the MIT license.
Large-Scale Worst-Case Topology Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhang, Di; Zhai, Xiaoya; Fu, Xiao-Ming; Wang, Heming; Liu, Ligang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
We propose a novel topology optimization method to efficiently minimize the maximum compliance for a high-resolution model bearing uncertain external loads. Central to this approach is a modified power method that can quickly compute the maximum eigenvalue to evaluate the worst-case compliance, enabling our method to be suitable for large-scale topology optimization. After obtaining the worst-case compliance, we use the adjoint variable method to perform the sensitivity analysis for updating the density variables. By iteratively computing the worst-case compliance, performing the sensitivity analysis, and updating the density variables, our algorithm achieves the optimized models with high efficiency. The capability and feasibility of our approach are demonstrated over various large-scale models. Typically, for a model of size 512×170×170 and 69934 loading nodes, our method took about 50 minutes on a desktop computer with an NVIDIA GTX 1080Ti graphics card with 11 GB memory.
Spatio-temporal Keyframe Control of Traffic Simulation using Coarse-to-Fine Optimization
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Han, Yi; Wang, He; Jin, Xiaogang; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
We present a novel traffic trajectory editing method which uses spatio-temporal keyframes to control vehicles during the simulation to generate desired traffic trajectories. By taking self-motivation, path following and collision avoidance into account, the proposed force-based traffic simulation framework updates vehicle's motions in both the Frenet coordinates and the Cartesian coordinates. With the way-points from users, lane-level navigation can be generated by reference path planning. With a given keyframe, the coarse-to-fine optimization is proposed to efficiently generate the plausible trajectory which can satisfy the spatio-temporal constraints. At first, a directed state-time graph constructed along the reference path is used to search for a coarse-grained trajectory by mapping the keyframe as the goal. Then, using the information extracted from the coarse trajectory as initialization, adjoint-based optimization is applied to generate a finer trajectory with smooth motions based on our force-based simulation. We validate our method with extensive experiments.
MoMaS: Mold Manifold Simulation for Real-time Procedural Texturing
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Maggioli, Filippo; Marin, Riccardo; Melzi, Simone; Rodolà, Emanuele; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
The slime mold algorithm has recently been under the spotlight thanks to its compelling properties studied across many disciplines like biology, computation theory, and artificial intelligence. However, existing implementations act only on planar surfaces, and no adaptation to arbitrary surfaces is available. Inspired by this gap, we propose a novel characterization of the mold algorithm to work on arbitrary curved surfaces. Our algorithm is easily parallelizable on GPUs and allows to model the evolution of millions of agents in real-time over surface meshes with several thousand triangles, while keeping the simplicity proper of the slime paradigm. We perform a comprehensive set of experiments, providing insights on stability, behavior, and sensibility to various design choices. We characterize a broad collection of behaviors with a limited set of controllable and interpretable parameters, enabling a novel family of heterogeneous and high-quality procedural textures. The appearance and complexity of these patterns are well-suited to diverse materials and scopes, and we add another layer of generalization by allowing different mold species to compete and interact in parallel.
Targeting Shape and Material in Lighting Design
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Usta, Baran; Pont, Sylvia; Eisemann, Elmar; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Product lighting design is a laborious and time-consuming task. With product illustrations being increasingly rendered, the lighting challenge transferred to the virtual realm. Our approach targets lighting design in the context of a scene with fixed objects, materials, and camera parameters, illuminated by environmental lighting. Our solution offers control over the depiction of material characteristics and shape details by optimizing the illuminating environment-map. To that end, we introduce a metric that assesses the shape and material cues in terms of the designed appearance. We formalize the process and support steering the outcome using additional design constraints. We illustrate our solution with several challenging examples.
Out-of-core Extraction of Curve Skeletons for Large Volumetric Models
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Chu, Yiyao; Wang, Wencheng; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Existing methods for skeleton extraction have limitations in terms of the amount of memory space available, as the model must be allocated to the random access memory. This challenges the treatment of out-of-core models. Although applying out-of-core simplification methods to the model can fit in memory, this would induce distortion of the model surface, and so causing the skeleton to be off-centered or changing the topological structure. In this paper, we propose an efficient out-of-core method for extracting skeletons from large volumetric models. The method takes a volumetric model as input and first computes an out-of-core distance transform. With the distance transform, we generate a medial mesh to capture the prominent features for skeleton extraction, which significantly reduces the data size and facilitates the process of large models. At last, we contract the medial mesh in an out-of-core fashion to generate the skeleton. Experimental results show that our method can efficiently extract high-quality curve skeletons from large volumetric models with small memory usage.
MODNet: Multi-offset Point Cloud Denoising Network Customized for Multi-scale Patches
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Huang, Anyi; Xie, Qian; Wang, Zhoutao; Lu, Dening; Wei, Mingqiang; Wang, Jun; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi-scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing question - if employing multi-scale geometric perception information to guide the network to utilize multi-scale information, can eliminate the severe surface degradation problem? To answer it, we propose a Multi-offset Denoising Network (MODNet) customized for multi-scale patches. First, we extract the low-level feature of three scales patches by patch feature encoders. Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement. Third, a multi-offset decoder regresses three scale offsets, which are guided by the multi-scale weights to predict the final displacement by weighting them adaptively. Experiments demonstrate that our method achieves new state-of-the-art performance on both synthetic and real-scanned datasets. Our code is publicly available at https://github.com/hay-001/MODNet.
Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Shen, Yiyang; Wang, Yongzhen; Wei, Mingqiang; Chen, Honghua; Xie, Haoran; Cheng, Gary; Wang, Fu Lee; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Real-world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMIsupervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN). Unlike most of existing methods, Semi-MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation; and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real-world rainy and clean images to bridge the gap between synthetic and real-world rain. Extensive experiments show clear improvements of our approach over twenty representative state-of-the-arts on both synthetic and real-world rainy images. Source code is available at https://github.com/syy-whu/Semi-MoreGAN.
Local Offset Point Cloud Transformer Based Implicit Surface Reconstruction
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Yang, Yan Xin; Zhang, San Guo; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Implicit neural representations, such as MLP, can well recover the topology of watertight object. However, MLP fails to recover geometric details of watertight object and complicated topology due to dealing with point cloud in a point-wise manner. In this paper, we propose a point cloud transformer called local offset point cloud transformer (LOPCT) as a feature fusion module. Before using MLP to learn the implicit function, the input point cloud is first fed into the local offset transformer, which adaptively learns the dependency of the local point cloud and obtains the enhanced features of each point. The feature-enhanced point cloud is then fed into the MLP to recover the geometric details and sharp features of watertight object and complex topology. Extensive reconstruction experiments of watertight object and complex topology demonstrate that our method achieves comparable or better results than others in terms of recovering sharp features and geometric details. In addition, experiments on watertight objects demonstrate the robustness of our method in terms of average result.
Joint Hand and Object Pose Estimation from a Single RGB Image using High-level 2D Constraints
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Song, Hao-Xuan; Mu, Tai-Jiang; Martin, Ralph R.; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Joint pose estimation of human hands and objects from a single RGB image is an important topic for AR/VR, robot manipulation, etc. It is common practice to determine both poses directly from the image; some recent methods attempt to improve the initial poses using a variety of contact-based approaches. However, few methods take the real physical constraints conveyed by the image into consideration, leading to less realistic results than the initial estimates. To overcome this problem, we make use of a set of high-level 2D features which can be directly extracted from the image in a new pipeline which combines contact approaches and these constraints during optimization. Our pipeline achieves better results than direct regression or contactbased optimization: they are closer to the ground truth and provide high quality contact.
Resolution-switchable 3D Semantic Scene Completion
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Luo, Shoutong; Sun, Zhengxing; Sun, Yunhan; Wang, Yi; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Semantic scene completion (SSC) aims to recover the complete geometric structure as well as the semantic segmentation results from partial observations. Previous works could only perform this task at a fixed resolution. To handle this problem, we propose a new method that can generate results at different resolutions without redesigning and retraining. The basic idea is to decouple the direct connection between resolution and network structure. To achieve this, we convert feature volume generated by SSC encoders into a resolution adaptive feature and decode this feature via point. We also design a resolution-adapted point sampling strategy for testing and a category-based point sampling strategy for training to further handle this problem. The encoder of our method can be replaced by existing SSC encoders. We can achieve better results at other resolutions while maintaining the same accuracy as the original resolution results. Code and data are available at https://github.com/lstcutong/ReS-SSC.
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.
A Wide Spectral Range Sky Radiance Model
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Vévoda, Petr; Bashford-Rogers, Tom; Kolářová, Monika; Wilkie, Alexander; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Pre-computed models of sky radiance are a tool to rapidly determine incident solar irradiance in applications as diverse as movie VFX, lighting simulation for architecture, experimental biology, and flight simulators. Several such models exist, but most provide data only for the visible range and, in some cases, for the near-UV. But for accurate simulations of photovoltaic plant yield and the thermal properties of buildings, a pre-computed reference sky model which covers the entire spectral range of terrestrial solar irradiance is needed: and this range is considerably larger than what extant models provide. We deliver this, and for a ground-based observer provide the three components of sky dome radiance, atmospheric transmittance, and polarisation. We also discuss the additional aspects that need to be taken into consideration when including the near-infrared in such a model. Additionally, we provide a simple standalone C++ implementation as well as an implementation with a GUI.
A Drone Video Clip Dataset and its Applications in Automated Cinematography
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Ashtari, Amirsaman; Jung, Raehyuk; Li, Mingxiao; Noh, Junyong; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Drones became popular video capturing tools. Drone videos in the wild are first captured and then edited by humans to contain aesthetically pleasing camera motions and scenes. Therefore, edited drone videos have extremely useful information for cinematography and for applications such as camera path planning to capture aesthetically pleasing shots. To design intelligent camera path planners, learning drone camera motions from these edited videos is essential. However, first, this requires to filter drone clips and extract their camera motions out of these edited videos that commonly contain both drone and non-drone content. Moreover, existing video search engines return the whole edited video as a semantic search result and cannot return only drone clips inside an edited video. To address this problem, we proposed the first approach that can automatically retrieve drone clips from an unlabeled video collection using high-level search queries, such as ''drone clips captured outdoor in daytime from rural places". The retrieved clips also contain camera motions, camera view, and 3D reconstruction of a scene that can help develop intelligent camera path planners. To train our approach, we needed numerous examples of edited drone videos. To this end, we introduced the first large-scale dataset composed of edited drone videos. This dataset is also used for training and validating our drone video filtering algorithm. Both quantitative and qualitative evaluations have confirmed the validity of our method.
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.
Fine-Grained Memory Profiling of GPGPU Kernels
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Buelow, Max von; Guthe, Stefan; Fellner, Dieter W.; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Memory performance is a crucial bottleneck in many GPGPU applications, making optimizations for hardware and software mandatory. While hardware vendors already use highly efficient caching architectures, software engineers usually have to organize their data accordingly in order to efficiently make use of these, requiring deep knowledge of the actual hardware. In this paper we present a novel technique for fine-grained memory profiling that simulates the whole pipeline of memory flow and finally accumulates profiling values in a way that the user retains information about the potential region in the GPU program by showing these values separately for each allocation. Our memory simulator turns out to outperform state-of-theart memory models of NVIDIA architectures by a magnitude of 2.4 for the L1 cache and 1.3 for the L2 cache, in terms of accuracy. Additionally, we find our technique of fine grained memory profiling a useful tool for memory optimizations, which we successfully show in case of ray tracing and machine learning applications.
Learning Dynamic 3D Geometry and Texture for Video Face Swapping
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Otto, Christopher; Naruniec, Jacek; Helminger, Leonhard; Etterlin, Thomas; Mignone, Graziana; Chandran, Prashanth; Zoss, Gaspard; Schroers, Christopher; Gross, Markus; Gotardo, Paulo; Bradley, Derek; Weber, Romann; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Face swapping is the process of applying a source actor's appearance to a target actor's performance in a video. This is a challenging visual effect that has seen increasing demand in film and television production. Recent work has shown that datadriven methods based on deep learning can produce compelling effects at production quality in a fraction of the time required for a traditional 3D pipeline. However, the dominant approach operates only on 2D imagery without reference to the underlying facial geometry or texture, resulting in poor generalization under novel viewpoints and little artistic control. Methods that do incorporate geometry rely on pre-learned facial priors that do not adapt well to particular geometric features of the source and target faces. We approach the problem of face swapping from the perspective of learning simultaneous convolutional facial autoencoders for the source and target identities, using a shared encoder network with identity-specific decoders. The key novelty in our approach is that each decoder first lifts the latent code into a 3D representation, comprising a dynamic face texture and a deformable 3D face shape, before projecting this 3D face back onto the input image using a differentiable renderer. The coupled autoencoders are trained only on videos of the source and target identities, without requiring 3D supervision. By leveraging the learned 3D geometry and texture, our method achieves face swapping with higher quality than when using offthe- shelf monocular 3D face reconstruction, and overall lower FID score than state-of-the-art 2D methods. Furthermore, our 3D representation allows for efficient artistic control over the result, which can be hard to achieve with existing 2D approaches.
NSTO: Neural Synthesizing Topology Optimization for Modulated Structure Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Zhong, Shengze; Punpongsanon, Parinya; Iwai, Daisuke; Sato, Kosuke; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
Nature evolves structures like honeycombs at optimized performance with limited material. These efficient structures can be artificially created with the collaboration of structural topology optimization and additive manufacturing. However, the extensive computation cost of topology optimization causes low mesh resolution, long solving time, and rough boundaries that fail to match the requirements for meeting the growing personal fabrication demands and printing capability. Therefore, we propose the neural synthesizing topology optimization that leverages a self-supervised coordinate-based network to optimize structures with significantly shorter computation time, where the network encodes the structural material layout as an implicit function of coordinates. Continuous solution space is further generated from optimization tasks under varying boundary conditions or constraints for users' instant inference of novel solutions. We demonstrate the system's efficacy for a broad usage scenario through numerical experiments and 3D printing.

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