基于外观与几何复杂度的3D高保真重建与跟踪新方法
GTR: Gaussian Splatting Tracking and Reconstruction of Unknown Objects Based on Appearance and Geometric Complexity
- 融合高斯点云与外观/几何联合追踪，自适应处理复杂物体
- 在对称、复杂结构等挑战性物体上实现稳定跟踪与高质量重建
- 适用于开放世界场景下的单目RGBD视频3D重建，适合工业与机器人应用
我们提出一种新型单目RGBD视频6-DoF物体跟踪与高质量3D重建方法。现有方法虽表现优异，但在对称、复杂几何或复杂外观的物体上仍面临挑战。为此，我们引入自适应方法，结合3D高斯点云（Gaussian Splatting）、混合外观/几何追踪以及关键帧选择机制，在多种复杂物体上实现了鲁棒跟踪与精确重建。此外，我们构建了一个涵盖此类挑战性物体的基准数据集，提供高质量标注以评估跟踪与重建性能。本方法在恢复高保真物体网格方面表现卓越，为单传感器在开放世界环境中的3D重建树立了新标准。
原文摘要 · Abstract (English)
We present a novel method for 6-DoF object tracking and high-quality 3D reconstruction from monocular RGBD video. Existing methods, while achieving impressive results, often struggle with complex objects, particularly those exhibiting symmetry, intricate geometry or complex appearance. To bridge these gaps, we introduce an adaptive method that combines 3D Gaussian Splatting, hybrid geometry/appearance tracking, and key frame selection to achieve robust tracking and accurate reconstructions across a diverse range of objects. Additionally, we present a benchmark covering these challenging object classes, providing high-quality annotations for evaluating both tracking and reconstruction performance. Our approach demonstrates strong capabilities in recovering high-fidelity object meshes, setting a new standard for single-sensor 3D reconstruction in open-world environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。