用投影法快速选最优视角，高效完整扫描未知物体
An Efficient Projection-Based Next-best-view Planning Framework for Reconstruction of Unknown Objects
- 将体素簇拟合为椭球，通过投影评估视角质量
- 相比传统方法效率提升10倍，覆盖效果相当
- 适合需要实时三维重建的机器人与工业场景
在工业和机器人应用中，高效且完整地获取物体的三维数据是一个基本问题。接下来最佳视角（NBV）规划的任务是根据当前数据推断下一个观测位姿，逐步实现完整的三维重建。然而，许多现有算法因使用射线投射而面临巨大的计算负担。为此，本文提出一种基于投影的NBV规划框架，能够在极快的速度下选择下一个最佳视角，同时确保物体的完整扫描。具体而言，该框架基于体素结构将不同类型的体素簇拟合为椭球，并结合全局分区策略，利用基于投影的视角质量评估函数从候选视角中选择最优解。这一过程替代了体素结构中的射线投射，显著提升了计算效率。仿真环境下的对比实验表明，所提框架在保持相近覆盖率的前提下，效率提升约10倍。真实世界实验也验证了该框架的高效性与可行性。
原文摘要 · Abstract (English)
Efficiently and completely capturing the three-dimensional data of an object is a fundamental problem in industrial and robotic applications. The task of next-best-view (NBV) planning is to infer the pose of the next viewpoint based on the current data, and gradually realize the complete three-dimensional reconstruction. Many existing algorithms, however, suffer a large computational burden due to the use of ray-casting. To address this, this paper proposes a projection-based NBV planning framework. It can select the next best view at an extremely fast speed while ensuring the complete scanning of the object. Specifically, this framework refits different types of voxel clusters into ellipsoids based on the voxel structure.Then, the next best view is selected from the candidate views using a projection-based viewpoint quality evaluation function in conjunction with a global partitioning strategy. This process replaces the ray-casting in voxel structures, significantly improving the computational efficiency. Comparative experiments with other algorithms in a simulation environment show that the framework proposed in this paper can achieve 10 times efficiency improvement on the basis of capturing roughly the same coverage. The real-world experimental results also prove the efficiency and feasibility of the framework.
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