Supervised Learning of the Next-Best-View for 3D Object Reconstruction
This addresses the challenge of optimal sensor placement for 3D reconstruction, which is incremental as it builds on existing methods with a new learning-based approach.
The paper tackles the next-best-view planning problem for 3D object reconstruction by proposing a supervised deep learning scheme that directly predicts sensor poses, and experiments validate its effectiveness.
Motivated by the advances in 3D sensing technology and the spreading of low-cost robotic platforms, 3D object reconstruction has become a common task in many areas. Nevertheless, the selection of the optimal sensor pose that maximizes the reconstructed surface is a problem that remains open. It is known in the literature as the next-best-view planning problem. In this paper, we propose a novel next-best-view planning scheme based on supervised deep learning. The scheme contains an algorithm for automatic generation of datasets and an original three-dimensional convolutional neural network (3D-CNN) used to learn the next-best-view. Unlike previous work where the problem is addressed as a search, the trained 3D-CNN directly predicts the sensor pose. We present a comparison of the proposed network against a similar net, and we present several experiments of the reconstruction of unknown objects validating the effectiveness of the proposed scheme.