用AV1视频运动矢量加速3D高斯点云生成，提升重建质量与速度
Efficient Dense Matching for Enhanced Gaussian Splatting Using AV1 Motion Vectors
- 利用AV1编码器内置运动矢量实现高效特征匹配
- 点云密度提升8倍，训练时间减少63%至基线质量
- 适合需要快速高精度3D重建的实时应用
3D高斯泼溅（3DGS）已成为实现实时、逼真场景重建的主流框架，相比神经辐射场（NeRF）有显著提速。然而其重建精度仍高度依赖初始点云质量。传统基于COLMAP的运动结构（SfM）虽能提供合理初始化，但计算开销大，且在无纹理区域常出现稀疏问题，影响后续重建精度与收敛速度。本文提出一种基于AV1视频编码器运动矢量的特征检测与匹配流水线，无需耗时的穷举匹配，即可保持几何鲁棒性。该方法生成的点云密度最高达传统SfM的八倍。实验表明，此增强初始化直接提升3DGS性能：VMAF评分提高9分，达到基线质量的平均训练时间减少63%。
原文摘要 · Abstract (English)
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/
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