用投影法替代射线追踪，高效规划三维重建的最优视角
PB-NBV: Efficient Projection-Based Next-Best-View Planning Framework for Reconstruction of Unknown Objects
- 将体素聚类拟合为椭球，通过投影评估视点质量
- 仿真中以低计算时间达成最高点云覆盖率
- 适合需快速重建未知物体的工业与机器人场景
完全获取物体的三维数据在工业和机器人应用中至关重要。接下来的最佳视角（NBV）规划任务是基于当前数据计算下一个最优视角，逐步完成物体的完整三维重建。然而，许多现有NBV规划算法因大量使用射线追踪而产生高昂的计算成本。本文框架首先根据体素结构将不同类型的体素聚类重新拟合为椭球；随后，结合全局分区策略，利用基于投影的视点质量评估函数从候选视角中选择下一个最优视角。该过程取代了大量射线追踪，显著提升了计算效率。仿真环境下的对比实验表明，本框架在计算时间较低的情况下实现了最高的点云覆盖率。真实世界实验也验证了该框架的高效性与可行性。本方法将开源，以惠及社区。
原文摘要 · Abstract (English)
Completely capturing the three-dimensional (3D) data of an object is essential in industrial and robotic applications. The task of next-best-view (NBV) planning is to calculate the next optimal viewpoint based on the current data, gradually achieving a complete 3D reconstruction of the object. However, many existing NBV planning algorithms incur heavy computational costs due to the extensive use of ray-casting. Specifically, this framework refits different types of voxel clusters into ellipsoids based on the voxel structure. Then, the next optimal viewpoint is selected from the candidate views using a projection-based viewpoint quality evaluation function in conjunction with a global partitioning strategy. This process replaces extensive ray-casting, significantly improving the computational efficiency. Comparison experiments in the simulation environment show that our framework achieves the highest point cloud coverage with low computational time compared to other frameworks. The real-world experiments also confirm the efficiency and feasibility of the framework. Our method will be made open source to benefit the community.
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。