Next Best View For Point-Cloud Model Acquisition: Bayesian Approximation and Uncertainty Analysis

TL;DR

This paper enhances point-cloud next best view prediction by integrating Bayesian dropout to estimate uncertainty, significantly improving accuracy and efficiency in 3D reconstruction tasks.

Contribution

It adapts a neural network with Bayesian dropout for uncertainty estimation in next best view prediction, improving accuracy and reducing errors in 3D reconstruction.

Findings

Prediction accuracy increased from 30% to 80%.

Uncertainty metrics effectively identified high-error predictions.

Proposed uncertainty-based methods slightly improved final predictions.

Abstract

The Next Best View problem is a computer vision problem widely studied in robotics. To solve it, several methodologies have been proposed over the years. Some, more recently, propose the use of deep learning models. Predictions obtained with the help of deep learning models naturally have some uncertainty associated with them. Despite this, the standard models do not allow for their quantification. However, Bayesian estimation theory contributed to the demonstration that dropout layers allow to estimate prediction uncertainty in neural networks. This work adapts the point-net-based neural network for Next-Best-View (PC-NBV). It incorporates dropout layers into the model's architecture, thus allowing the computation of the uncertainty estimate associated with its predictions. The aim of the work is to improve the network's accuracy in correctly predicting the next best viewpoint, proposing a way to make the 3D reconstruction process more efficient. Two uncertainty measurements capable of reflecting the prediction's error and accuracy, respectively, were obtained. These enabled the reduction of the model's error and the increase in its accuracy from 30\% to 80\% by identifying and disregarding predictions with high values of uncertainty. Another method that directly uses these uncertainty metrics to improve the final prediction was also proposed. However, it showed very residual improvements.