44-Issue 7

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https://diglib.eg.org/handle/10.2312/3607235

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Browsing 44-Issue 7 by Subject "based models"

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    FAHNet: Accurate and Robust Normal Estimation for Point Clouds via Frequency-Aware Hierarchical Geometry
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Wang, Chengwei; Wu, Wenming; Fei, Yue; Zhang, Gaofeng; Zheng, Liping; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
    Point cloud normal estimation underpins many 3D vision and graphics applications. Precise normal estimation in regions of sharp curvature and high-frequency variation remains a major bottleneck; existing learning-based methods still struggle to isolate fine geometry details under noise and uneven sampling. We present FAHNet, a novel frequency-aware hierarchical network that precisely tackles those challenges. Our Frequency-Aware Hierarchical Geometry (FAHG) feature extraction module selectively amplifies and merges cross-scale cues, ensuring that both fine-grained local features and sharp structures are faithfully represented. Crucially, a dedicated Frequency-Aware geometry enhancement (FA) branch intensifies sensitivity to abrupt normal transitions and sharp features, preventing the common over-smoothing limitation. Extensive experiments on synthetic benchmarks (PCPNet, FamousShape) and real-world scans (SceneNN) demonstrate that FAHNet outperforms state-of-the-art approaches in normal estimation accuracy. Ablation studies further quantify the contribution of each component, and downstream surface reconstruction results validate the practical impact of our design.
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    Gaussians on their Way: Wasserstein-Constrained 4D Gaussian Splatting with State-Space Modeling
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Deng, Junli; , Ping Shi; Luo, Yihao; Li, Qipei; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
    Dynamic scene rendering has taken a leap forward with the rise of 4D Gaussian Splatting, but there is still one elusive challenge: how to make 3D Gaussians move through time as naturally as they would in the real world, all while keeping the motion smooth and consistent. In this paper, we present an approach that blends state-space modeling with Wasserstein geometry, enabling a more fluid and coherent representation of dynamic scenes. We introduce a State Consistency Filter that merges prior predictions with the current observations, enabling Gaussians to maintain coherent trajectories over time. We also employ Wasserstein Consistency Constraint to ensure smooth, consistent updates of Gaussian parameters, reducing motion artifacts. Lastly, we leverage Wasserstein geometry to capture both translational motion and shape deformations, creating a more geometrically consistent model for dynamic scenes. Our approach models the evolution of Gaussians along geodesics on the manifold of Gaussian distributions, achieving smoother, more realistic motion and stronger temporal coherence. Experimental results show consistent improvements in rendering quality and efficiency.
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    IPFNet: Implicit Primitive Fitting for Robust Point Cloud Segmentation
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Zhou, Shengdi; Zan, Xiaoqiang; Zhou, Bin; Christie, Marc; Pietroni, Nico; Wang, Yu-Shuen
    The segmentation and fitting of geometric primitives from point clouds is a widely adopted approach for modelling the underlying geometric structure of objects in reverse engineering and numerous graphics applications. Existing methods either overlook the role of geometric information in assisting segmentation or incorporate reconstruction losses without leveraging modern neural implicit field representations, leading to limited robustness against noise and weak expressive power in reconstruction. We propose a point cloud segmentation and fitting framework based on neural implicit representations, fully leveraging neural implicit fields' expressive power and robustness. The key idea is the unification of geometric representation within a neural implicit field framework, enabling seamless integration of geometric loss for improved performance. In contrast to previous approaches that focus solely on clustering in the feature embedding space, our method enhances instance segmentation through semanticaware point embeddings and simultaneously improves semantic predictions via instance-level feature fusion. Furthermore, we incorporate 3D-specific cues such as spatial dimensions and geometric connectivity, which are uniquely informative in the 3D domain. Extensive experiments and comparisons against previous methods demonstrate our robustness and superiority.