arXiv:2504.09062cs.CV2025-04被引 1

用过渡平面解决全景图3D重建中的拼接畸变问题

You Need a Transition Plane: Bridging Continuous Panoramic 3D Reconstruction with Perspective Gaussian Splatting

  • 引入过渡平面平滑立方体面间渲染方向
  • 在多个数据集上实现更优的视觉一致性与细节还原
  • 适合做全景3D重建和视觉一致优化的研究者

近年来,利用先进3D高斯点云(3DGS)技术从单张全景图像重建场景受到广泛关注。全景图像提供360×180°视场,可一次性捕捉完整场景。但其严重畸变使得直接将3D高斯点云投影到2D等距圆柱投影空间极具挑战。将等距圆柱图转为立方体贴图部分缓解该问题,却带来新的投影畸变与立方体面边界不连续性。为此,我们提出新框架TPGS,连接连续全景3D重建与透视高斯点云渲染。首先,在相邻立方体面之间引入过渡平面,实现点云投射方向更平滑,减轻边界区域优化歧义。其次,提出面内-面间联合优化策略:先在各立方体面内优化3D高斯点云,再在拼接后的全景空间中微调。此外,引入球面采样技术消除可见拼接缝。在室内、室外、第一人称及漫游类基准数据集上的大量实验表明,本方法优于现有最先进方法。代码与模型将开源于https://github.com/zhijieshen-bjtu/TPGS。

原文摘要 · Abstract (English)

Recently, reconstructing scenes from a single panoramic image using advanced 3D Gaussian Splatting (3DGS) techniques has attracted growing interest. Panoramic images offer a 360$\times$ 180 field of view (FoV), capturing the entire scene in a single shot. However, panoramic images introduce severe distortion, making it challenging to render 3D Gaussians into 2D distorted equirectangular space directly. Converting equirectangular images to cubemap projections partially alleviates this problem but introduces new challenges, such as projection distortion and discontinuities across cube-face boundaries. To address these limitations, we present a novel framework, named TPGS, to bridge continuous panoramic 3D scene reconstruction with perspective Gaussian splatting. Firstly, we introduce a Transition Plane between adjacent cube faces to enable smoother transitions in splatting directions and mitigate optimization ambiguity in the boundary region. Moreover, an intra-to-inter face optimization strategy is proposed to enhance local details and restore visual consistency across cube-face boundaries. Specifically, we optimize 3D Gaussians within individual cube faces and then fine-tune them in the stitched panoramic space. Additionally, we introduce a spherical sampling technique to eliminate visible stitching seams. Extensive experiments on indoor and outdoor, egocentric, and roaming benchmark datasets demonstrate that our approach outperforms existing state-of-the-art methods. Code and models will be available at https://github.com/zhijieshen-bjtu/TPGS.

3D重建高斯点云全景图像渲染优化

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