SCVP: Learning One-Shot View Planning via Set Covering for Unknown Object Reconstruction

Abstract

The view planning (VP) problem in robotic active vision enables a robot system to automatically perform object reconstruction tasks. Lacking prior knowledge, next-best-view (NBV) methods are typically used to plan a view sequence, with the goal of covering as many object surface areas as possible in an unknown environment. However, such methods have two problems: (1) they are unable to perform global path planning; and (2) the reconstruction process is inefficient because of time-consuming ray casting and high movement cost. We propose a neural network, SCVP, to pre-learn prior knowledge via set covering (SC) based training so as to achieve one-shot view planning. The SCVP network takes the volumetric occupancy grid as input and directly predicts a small (ideally minimum) number of views that cover all surface areas. Given object 3D models as a priori geometric knowledge, the training dataset is automatically labeled by the set covering optimization method. We propose a global path planning method to reconstruct objects without redundant movement. Comparative experiments on multiple datasets of 3D models show that, under the condition of similar or better surface coverage, the proposed method can outperform state-of-the-art NBV methods in terms of movement cost and inference time. Real-world experiments confirm that the proposed method can achieve faster object reconstruction than other methods.