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

arXiv: https://arxiv.org/abs/2411.01734 · HTML: https://mlsanity.com/paper/2411.01734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Modelling Uncertainty in Deep Learning for Camera Relocalization](https://mlsanity.com/paper/1509.05909/) (2015)
- [Patch-based Progressive 3D Point Set Upsampling](https://mlsanity.com/paper/1811.11286/) (2018)
- [Supervised Learning of the Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/1905.05833/) (2019)
- [Next-best-view Regression using a 3D Convolutional Neural Network](https://mlsanity.com/paper/2101.09397/) (2021)

## Abstract (arXiv)

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.