MAGiSt3R：基于单目RGB视频的多智能体前馈3D重建
MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos
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中文总结 AI 辅助
研究基于单目RGB视频的多智能体3D重建问题，核心方法是用3R家族前馈模型和MAGMA合并模型，并进行姿态图优化，主要贡献是在合成和真实数据集上验证该框架相比现有方法有更高重建和相机跟踪精度。
中文摘要 AI 辅助
本文提出了MAGiSt3R，一个多智能体3D重建框架，可对单目RGB视频进行重建和相机跟踪，帧率近10 FPS。它依赖3R家族的前馈模型处理RGB视频并回归局部点图，以及合并模型MAGMA在智能体内外合并局部图以获最终全局点图。此外，还进行姿态图优化以减轻相机漂移。在合成和真实数据集上评估，结果显示其相比现有方法有更高重建和跟踪精度。
英文摘要
This paper presents MAGiSt3R, a multi-agent 3D reconstruction framework performing reconstruction and camera tracking for monocular RGB videos at almost 10 FPS. MAGiSt3R relies on a feed-forward model from the 3R family to process RGB videos and regress local point maps, and on a merging model, MAGMA, that combines local maps at both intra-agent and inter-agent levels to obtain the final global point map. Furthermore, MAGiSt3R performs pose graph optimization to mitigate cumulative camera drift occurring along the feed-forward pipeline. We evaluate MAGiSt3R on both synthetic and real-world datasets, demonstrating its superior reconstruction and camera tracking accuracy compared to state-of-the-art approaches.
发表机构
- University of Bologna（博洛尼亚大学）
- The University of Hong Kong（香港大学）
- Southeast University（东南大学）
- Monash University（莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。
