43-Issue 7

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607047

Browse

Browsing 43-Issue 7 by Subject "based rendering"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Wang, Chao; Wolski, Krzysztof; Kerbl, Bernhard; Serrano, Ana; Bemama, Mojtaba; Seidel, Hans-Peter; Myszkowski, Karol; Leimkühler, Thomas; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    Radiance field methods represent the state of the art in reconstructing complex scenes from multi-view photos. However, these reconstructions often suffer from one or both of the following limitations: First, they typically represent scenes in low dynamic range (LDR), which restricts their use to evenly lit environments and hinders immersive viewing experiences. Secondly, their reliance on a pinhole camera model, assuming all scene elements are in focus in the input images, presents practical challenges and complicates refocusing during novel-view synthesis. Addressing these limitations, we present a lightweight method based on 3D Gaussian Splatting that utilizes multi-view LDR images of a scene with varying exposure times, apertures, and focus distances as input to reconstruct a high-dynamic-range (HDR) radiance field. By incorporating analytical convolutions of Gaussians based on a thin-lens camera model as well as a tonemapping module, our reconstructions enable the rendering of HDR content with flexible refocusing capabilities. We demonstrate that our combined treatment of HDR and depth of field facilitates real-time cinematic rendering, outperforming the state of the art.
  • Thumbnail Image
    Item
    GauLoc: 3D Gaussian Splatting-based Camera Relocalization
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Xin, Zhe; Dai, Chengkai; Li, Ying; Wu, Chenming; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    3D Gaussian Splatting (3DGS) has emerged as a promising representation for scene reconstruction and novel view synthesis for its explicit representation and real-time capabilities. This technique thus holds immense potential for use in mapping applications. Consequently, there is a growing need for an efficient and effective camera relocalization method to complement the advantages of 3DGS. This paper presents a camera relocalization method, namely GauLoc, in a scene represented by 3DGS. Unlike previous methods that rely on pose regression or photometric alignment, our proposed method leverages the differential rendering capability provided by 3DGS. The key insight of our work is the proposed implicit featuremetric alignment, which effectively optimizes the alignment between rendered keyframes and the query frames, and leverages the epipolar geometry to facilitate the convergence of camera poses conditioned explicit 3DGS representation. The proposed method significantly improves the relocalization accuracy even in complex scenarios with large initial camera rotation and translation deviations. Extensive experiments validate the effectiveness of our proposed method, showcasing its potential to be applied in many realworld applications. Source code will be released at https://github.com/xinzhe11/GauLoc.
  • Thumbnail Image
    Item
    GS-Octree: Octree-based 3D Gaussian Splatting for Robust Object-level 3D Reconstruction Under Strong Lighting
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Li, Jiaze; Wen, Zhengyu; Zhang, Luo; Hu, Jiangbei; Hou, Fei; Zhang, Zhebin; He, Ying; Chen, Renjie; Ritschel, Tobias; Whiting, Emily
    The 3D Gaussian Splatting technique has significantly advanced the construction of radiance fields from multi-view images, enabling real-time rendering. While point-based rasterization effectively reduces computational demands for rendering, it often struggles to accurately reconstruct the geometry of the target object, especially under strong lighting conditions. Strong lighting can cause significant color variations on the object's surface when viewed from different directions, complicating the reconstruction process. To address this challenge, we introduce an approach that combines octree-based implicit surface representations with Gaussian Splatting. Initially, it reconstructs a signed distance field (SDF) and a radiance field through volume rendering, encoding them in a low-resolution octree. This initial SDF represents the coarse geometry of the target object. Subsequently, it introduces 3D Gaussians as additional degrees of freedom, which are guided by the initial SDF. In the third stage, the optimized Gaussians enhance the accuracy of the SDF, enabling the recovery of finer geometric details compared to the initial SDF. Finally, the refined SDF is used to further optimize the 3D Gaussians via splatting, eliminating those that contribute little to the visual appearance. Experimental results show that our method, which leverages the distribution of 3D Gaussians with SDFs, reconstructs more accurate geometry, particularly in images with specular highlights caused by strong lighting. The source code can be downloaded from https://github.com/LaoChui999/GS-Octree.