# Supervised Learning of the Next-Best-View for 3D Object Reconstruction

## TL;DR

This paper introduces a supervised deep learning approach using a 3D-CNN to directly predict the next-best-view for 3D object reconstruction, improving efficiency over traditional search-based methods.

## Contribution

It presents a novel 3D-CNN model for next-best-view prediction and an automatic dataset generation method, advancing 3D reconstruction planning techniques.

## Key findings

- The 3D-CNN outperforms similar networks in predicting sensor poses.
- The approach effectively reconstructs unknown objects.
- Experimental results validate the method's accuracy and efficiency.

## Abstract

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.