arXiv 2503.022232025-03-04cs.CV

DQO-MAP：基于高斯泼溅的双二次曲面多物体建图

DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting

Research Institute of Intelligent Control and Systems, Harbin Institute of Technology（哈尔滨工业大学智能控制系统研究院）

Haoyuan Li, Ziqin Ye, Yue Hao, Weiyang Lin, Chao Ye

AI总结：

本文提出DQO-MAP，一个集成物体位姿估计与重建的物体SLAM系统，利用3D Gaussian Splatting进行高保真重建和二次曲面进行精确位姿估计，通过CPU/GPU分工提升效率，实现快速物体提取，在精度、重建质量和计算效率上表现优异。

摘要：

准确的物体感知对于物体导航等机器人应用至关重要。本文提出DQO-MAP，一个无缝集成物体位姿估计与重建的新型物体SLAM系统。我们采用3D Gaussian Splatting进行高保真物体重建，并利用二次曲面进行精确的物体位姿估计。两者的管理在CPU上处理，而优化在GPU上执行，显著提高了系统效率。通过将物体与唯一ID关联，我们的系统能够从场景中快速提取物体。在物体重建和位姿估计上的大量实验结果表明，DQO-MAP在精度、重建质量和计算效率方面取得了出色的性能。代码和数据集可在 https://github.com/LiHaoy-ux/DQO-MAP 获取。

英文摘要：

Accurate object perception is essential for robotic applications such as object navigation. In this paper, we propose DQO-MAP, a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction. We employ 3D Gaussian Splatting for high-fidelity object reconstruction and leverage quadrics for precise object pose estimation. Both of them management is handled on the CPU, while optimization is performed on the GPU, significantly improving system efficiency. By associating objects with unique IDs, our system enables rapid object extraction from the scene. Extensive experimental results on object reconstruction and pose estimation demonstrate that DQO-MAP achieves outstanding performance in terms of precision, reconstruction quality, and computational efficiency. The code and dataset are available at: https://github.com/LiHaoy-ux/DQO-MAP.

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。