Now showing items 10055-10074 of 17315

    • Neural Data Exploration with Force Feedback 

      Raya, Laura; Otaduy, Miguel A.; García, Marcos (The Eurographics Association, 2021)
      The behavior of the brain depends to a large extend on its neural structure. Therefore, understanding this neural topology is a high-priority research line for neurobiologists. Due to complexity of the brain's neural ...
    • Neural Deformable Cone Beam CT 

      Birklein, Lukas; Schömer, Elmar; Brylka, Robert; Schwanecke, Ulrich; Schulze, Ralf (The Eurographics Association, 2023)
      In oral and maxillofacial cone beam computed tomography (CBCT), patient motion is frequently observed and, if not accounted for, can severely affect the usability of the acquired images. We propose a highly flexible, data ...
    • Neural Denoising for Deep-Z Monte Carlo Renderings 

      Zhang, Xianyao; Röthlin, Gerhard; Zhu, Shilin; Aydin, Tunç Ozan; Salehi, Farnood; Gross, Markus; Papas, Marios (The Eurographics Association and John Wiley & Sons Ltd., 2024)
      We present a kernel-predicting neural denoising method for path-traced deep-Z images that facilitates their usage in animation and visual effects production. Deep-Z images provide enhanced flexibility during compositing ...
    • Neural Denoising for Path Tracing of Medical Volumetric Data 

      Hofmann, Nikolai; Martschinke, Jana; Engel, Klaus; Stamminger, Marc (ACM, 2020)
      In this paper, we transfer machine learning techniques previously applied to denoising surface-only Monte Carlo renderings to path-traced visualizations of medical volumetric data. In the domain of medical imaging, path-traced ...
    • Neural Denoising for Spectral Monte Carlo Rendering 

      Rouphael, Robin; Noizet, Mathieu; Prévost, Stéphanie; Deleau, Hervé; Steffenel, Luiz-Angelo; Lucas, Laurent (The Eurographics Association, 2022)
      Spectral Monte Carlo (MC) rendering is still to be largely adopted partially due to the specific noise, called color noise, induced by wavelength-dependent phenomenons. Motivated by the recent advances in Monte Carlo noise ...
    • Neural Denoising with Layer Embeddings 

      Munkberg, Jacob; Hasselgren, Jon (The Eurographics Association and John Wiley & Sons Ltd., 2020)
      We propose a novel approach for denoising Monte Carlo path traced images, which uses data from individual samples rather than relying on pixel aggregates. Samples are partitioned into layers, which are filtered separately, ...
    • Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles 

      Farokhmanesh, Fatemeh; Höhlein, Kevin; Neuhauser, Christoph; Necker, Tobias; Weissmann, Martin; Miyoshi, Takemasa; Westermann, Rüdiger (The Eurographics Association, 2023)
      We present neural dependence fields (NDFs) - the first neural network that learns to compactly represent and efficiently reconstruct the statistical dependencies between the values of physical variables at different spatial ...
    • Neural Fields in Visual Computing and Beyond 

      Xie, Yiheng; Takikawa, Towaki; Saito, Shunsuke; Litany, Or; Yan, Shiqin; Khan, Numair; Tombari, Federico; Tompkin, James; Sitzmann, Vincent; Sridhar, Srinath (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      Recent advances in machine learning have led to increased interest in solving visual computing problems using methods that employ coordinate-based neural networks. These methods, which we call neural fields, parameterize ...
    • Neural Flow Map Reconstruction 

      Sahoo, Saroj; Lu, Yuzhe; Berger, Matthew (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      In this paper we present a reconstruction technique for the reduction of unsteady flow data based on neural representations of time-varying vector fields. Our approach is motivated by the large amount of data typically ...
    • Neural Free-Viewpoint Relighting for Glossy Indirect Illumination 

      Raghavan, Nithin; Xiao, Yan; Lin, Kai-En; Sun, Tiancheng; Bi, Sai; Xu, Zexiang; Li, Tzu-Mao; Ramamoorthi, Ravi (The Eurographics Association and John Wiley & Sons Ltd., 2023)
      Precomputed Radiance Transfer (PRT) remains an attractive solution for real-time rendering of complex light transport effects such as glossy global illumination. After precomputation, we can relight the scene with new ...
    • Neural Garment Dynamics via Manifold-Aware Transformers 

      Li, Peizhuo; Wang, Tuanfeng Y.; Kesdogan, Timur Levent; Ceylan, Duygu; Sorkine-Hornung, Olga (The Eurographics Association and John Wiley & Sons Ltd., 2024)
      Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. However, existing approaches often focus on modeling garments with respect ...
    • Neural Impostor: Editing Neural Radiance Fields with Explicit Shape Manipulation 

      Liu, Ruiyang; Xiang, Jinxu; Zhao, Bowen; Zhang, Ran; Yu, Jingyi; Zheng, Changxi (The Eurographics Association and John Wiley & Sons Ltd., 2023)
      Neural Radiance Fields (NeRF) have significantly advanced the generation of highly realistic and expressive 3D scenes. However, the task of editing NeRF, particularly in terms of geometry modification, poses a significant ...
    • Neural Intersection Function 

      Fujieda, Shin; Kao, Chih Chen; Harada, Takahiro (The Eurographics Association, 2023)
      The ray casting operation in the Monte Carlo ray tracing algorithm usually adopts a bounding volume hierarchy (BVH) to accelerate the process of finding intersections to evaluate visibility. However, its characteristics ...
    • Neural Mesh Reconstruction 

      Chen, Zhiqin (Simon Fraser University, 2023-06-16)
      Deep learning has revolutionized the field of 3D shape reconstruction, unlocking new possibilities and achieving superior performance compared to traditional methods. However, despite being the dominant 3D shape representation ...
    • Neural Modelling of Flower Bas‐relief from 2D Line Drawing 

      Zhang, Yu‐Wei; Wang, Jinlei; Wang, Wenping; Chen, Yanzhao; Liu, Hui; Ji, Zhongping; Zhang, Caiming (© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021)
      Different from other types of bas‐reliefs, a flower bas‐relief contains a large number of depth‐discontinuity edges. Most existing line‐based methods reconstruct free‐form surfaces by ignoring the depth‐discontinuities, ...
    • Neural Moment Transparency 

      Tsopouridis, Grigoris; Vasilakis, Andreas Alexandros; Fudos, Ioannis (The Eurographics Association, 2024)
      We have developed a machine learning approach to efficiently compute per-fragment transmittance, using transmittance composed and accumulated with moment statistics, on a fragment shader. Our approach excels in achieving ...
    • Neural Motion Compression with Frequency-adaptive Fourier Feature Network 

      Tojo, Kenji; Chen, Yifei; Umetani, Nobuyuki (The Eurographics Association, 2022)
      We present a neural-network-based compression method to alleviate the storage cost of motion capture data. Human motions such as locomotion, often consist of periodic movements. We leverage this periodicity by applying ...
    • Neural Networks for Digital Materials and Radiance Encoding 

      Rodriguez-Pardo, Carlos (2023-07)
      Realistic virtual scenes are becoming increasingly prevalent in our society, with a wide range of applications in areas such as manufacturing, architecture, fashion design, and entertainment, including movies, video games, ...
    • Neural Path Sampling for Rendering Pure Specular Light Transport 

      Yu, Rui; Dong, Yue; Kong, Youkang; Tong, Xin (© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024)
      Multi‐bounce, pure specular light paths produce complex lighting effects, such as caustics and sparkle highlights, which are challenging to render due to their sparse and diverse nature. We introduce a learning‐based method ...
    • Neural Precomputed Radiance Transfer 

      Rainer, Gilles; Bousseau, Adrien; Ritschel, Tobias; Drettakis, George (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      Recent advances in neural rendering indicate immense promise for architectures that learn light transport, allowing efficient rendering of global illumination effects once such methods are trained. The training phase of ...