高效密集匹配用于增强的高斯点云拼接使用AV1运动向量
Efficient Dense Matching for Enhanced Gaussian Splatting Using AV1 Motion Vectors
AI总结：
本文提出基于AV1的特征检测与匹配流程，减少SfM计算开销，生成更密集的点云，提升3DGS性能，VMAF提升9点，训练时间减少63%。
摘要：
3D高斯点云拼接（3DGS）已成为实时逼真场景重建的突出框架，相比神经辐射场（NeRF）具有显著的速度提升。然而，3DGS表示的保真度严重依赖于初始点云的质量。虽然标准结构从运动（SfM）管道使用COLMAP提供足够的初始化，但它们经常遭受高计算成本和纹理缺失区域的稀疏性，从而降低后续重建精度和收敛速度。在本文中，我们引入基于AV1的特征检测和匹配流程，显著减少SfM处理开销。通过利用AV1视频编解码器固有的运动向量，我们避免了计算昂贵的穷举匹配，同时保持几何鲁棒性。我们的流程生成显著更密集的点云，比传统SfM多多达八倍的点。我们证明这种增强的初始化直接提高了3DGS性能，导致VMAF提升9点，平均训练时间减少63%以达到基线质量。项目页面：https://sigmedia.tv/AV1-3DGS.github.io/
英文摘要：
3D Gaussian Splatting (3DGS) has emerged as a prominent framework for real-time, photorealistic scene reconstruction, offering significant speed-ups over Neural Radiance Fields (NeRF). However, the fidelity of 3DGS representations remains heavily dependent on the quality of the initial point cloud. While standard Structure-from-Motion (SfM) pipelines using COLMAP provide adequate initialisation, they often suffer from high computational costs and sparsity in textureless regions, which degrades subsequent reconstruction accuracy and convergence speed. In this work, we introduce an AV1-based feature detection and matching pipeline that significantly reduces SfM processing overhead. By leveraging motion vectors inherent to the AV1 video codec, we bypass computationally expensive exhaustive matching while maintaining geometric robustness. Our pipeline produces substantially denser point clouds, with up to eight times as many points as classical SfM. We demonstrate that this enhanced initialisation directly improves 3DGS performance, yielding an 9-point increase in VMAF and a 63% average reduction in training time required to reach baseline quality. The project page: https://sigmedia.tv/AV1-3DGS.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
