无需原始图像，直接将3D点云或网格转为高质量3D高斯点云模型
Transcoding a 3D Gaussian Splatting Model from a Plenoptic Point Cloud or Mesh without the Original Multi-view Images
- 通过端到端流程从3D点云/网格重建3D高斯点云模型
- 生成模型视觉质量高，点数比原始点云少得多
- 自定义初始化加速收敛，提升表面清晰度，适合无原始图像的场景
本文提出一种端到端的转码流程，可在缺少原始多视角图像的情况下，从已有的3D光场点云或网格模型生成3D高斯点云（3DGS）模型。我们设计了专用初始化策略，通过约束确保最终3DGS模型与输入点云或网格表面高度对齐。在高质量标准光场点云数据集上的测试表明，该方法生成的3DGS模型具有高视觉质量，且使用的高斯点数量远少于原始密集点云中的点数。此外，相比通常用于3DGS学习的SfM-based默认初始化，我们的自定义初始化显著加快收敛速度，并实现更清晰的表面表示。
原文摘要 · Abstract (English)
In this paper, we propose an end-to-end transcoding pipeline, to create 3D Gaussian splatting (3DGS) models from existing 3D plenoptic point cloud or mesh models, when the original multi-view images of the captured 3D object or scene are not available. We also propose a custom initialisation to guide the 3DGS model learning, with constraints to ensure that the final 3DGS model aligns closely with the input point cloud or mesh surface. Tests on a high-quality, standard plenoptic point cloud dataset show that our pipeline produces 3DGS models of high visual quality, with many fewer splats than points in the original dense point clouds. Additionally, our custom initialisation leads to much faster convergence and cleaner surface representation than when starting from the default SfM-based initialisation that is typically used for 3DGS model learning.
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