44-Issue 4

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https://diglib.eg.org/handle/10.2312/3607198

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Browsing 44-Issue 4 by Subject "based models"

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    Multiview Geometric Regularization of Gaussian Splatting for Accurate Radiance Fields
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Kim, Jungeon; Park, Geonsoo; Lee, Seungyong; Wang, Beibei; Wilkie, Alexander
    Recent methods, such as 2D Gaussian Splatting and Gaussian Opacity Fields, have aimed to address the geometric inaccuracies of 3D Gaussian Splatting while retaining its superior rendering quality. However, these approaches still struggle to reconstruct smooth and reliable geometry, particularly in scenes with significant color variation across viewpoints, due to their per-point appearance modeling and single-view optimization constraints. In this paper, we propose an effective multiview geometric regularization strategy that integrates multiview stereo (MVS) depth, RGB, and normal constraints into Gaussian Splatting initialization and optimization. Our key insight is the complementary relationship between MVS-derived depth points and Gaussian Splatting-optimized positions: MVS robustly estimates geometry in regions of high color variation through local patch-based matching and epipolar constraints, whereas Gaussian Splatting provides more reliable and less noisy depth estimates near object boundaries and regions with lower color variation. To leverage this insight, we introduce a median depthbased multiview relative depth loss with uncertainty estimation, effectively integrating MVS depth information into Gaussian Splatting optimization. We also propose an MVS-guided Gaussian Splatting initialization to avoid Gaussians falling into suboptimal positions. Extensive experiments validate that our approach successfully combines these strengths, enhancing both geometric accuracy and rendering quality across diverse indoor and outdoor scenes.
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    SPaGS: Fast and Accurate 3D Gaussian Splatting for Spherical Panoramas
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Li, Junbo; Hahlbohm, Florian; Scholz, Timon; Eisemann, Martin; Tauscher, Jan-Philipp; Magnor, Marcus; Wang, Beibei; Wilkie, Alexander
    In this paper we propose SPaGS, a high-quality, real-time free-viewpoint rendering approach from 360-degree panoramic images. While existing methods building on Neural Radiance Fields or 3D Gaussian Splatting have difficulties to achieve real-time frame rates and high-quality results at the same time, SPaGS combines the advantages of an explicit 3D Gaussian-based scene representation and ray casting-based rendering to attain fast and accurate results. Central to our new approach is the exact calculation of axis-aligned bounding boxes for spherical images that significantly accelerates omnidirectional ray casting of 3D Gaussians. We also present a new dataset consisting of ten real-world scenes recorded with a drone that incorporates both calibrated 360-degree panoramic images as well as perspective images captured simultaneously, i.e., with the same flight trajectory. Our evaluation on this new dataset as well as established benchmarks demonstrates that SPaGS excels over state-of-the-art methods in terms of both rendering quality and speed.