# Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validation

arXiv: https://arxiv.org/abs/2106.06684 · HTML: https://mlsanity.com/paper/2106.06684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [A Unified Framework for Multi-View Multi-Class Object Pose Estimation](https://mlsanity.com/paper/1803.08103/) (2018)
- [Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking](https://mlsanity.com/paper/1912.12125/) (2019)
- [Single Shot 6D Object Pose Estimation](https://mlsanity.com/paper/2004.12729/) (2020)
- [Defining the Pose of any 3D Rigid Object and an Associated Distance](https://mlsanity.com/paper/1612.04631/) (2016)
- [Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk](https://mlsanity.com/paper/1806.08129/) (2018)
- [CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation](https://mlsanity.com/paper/1909.13476/) (2019)
- [Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios](https://mlsanity.com/paper/1806.03891/) (2018)

## Abstract (arXiv)

This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Sil'eane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average.