An Efficient Projection-Based Next-best-view Planning Framework for Reconstruction of Unknown Objects

TL;DR

This paper introduces a fast, projection-based next-best-view planning framework that significantly improves the efficiency of 3D object reconstruction by replacing ray-casting with a voxel projection method.

Contribution

It presents a novel voxel refitting and viewpoint evaluation approach that drastically reduces computational load while maintaining complete object scanning.

Findings

Achieves 10 times faster reconstruction in simulations.

Maintains similar coverage with significantly reduced computation.

Proven effective in real-world experiments.

Abstract

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.