# Towards Deep Learning-based 6D Bin Pose Estimation in 3D Scans

arXiv: https://arxiv.org/abs/2112.09598 · HTML: https://mlsanity.com/paper/2112.09598/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019)
- [Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation](https://mlsanity.com/paper/1908.07433/) (2019)
- [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019)
- [BOP Challenge 2020 on 6D Object Localization](https://mlsanity.com/paper/2009.07378/) (2020)
- [DPOD: 6D Pose Object Detector and Refiner](https://mlsanity.com/paper/1902.11020/) (2019)
- [EPOS: Estimating 6D Pose of Objects with Symmetries](https://mlsanity.com/paper/2004.00605/) (2020)
- [EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach](https://mlsanity.com/paper/2011.04307/) (2020)
- [On Object Symmetries and 6D Pose Estimation from Images](https://mlsanity.com/paper/1908.07640/) (2019)
- [MaskedFusion: Mask-based 6D Object Pose Estimation](https://mlsanity.com/paper/1911.07771/) (2019)
- [Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search](https://mlsanity.com/paper/1710.08577/) (2017)

## Built on by (most cited first, 4 of 4)

- [On Representation of 3D Rotation in the Context of Deep Learning](https://mlsanity.com/paper/2410.10350/) (2024)
- [Novel Synthetic Data Tool for Data-Driven Cardboard Box Localization](https://mlsanity.com/paper/2305.05215/) (2023)
- [Enhancement of 3D Camera Synthetic Training Data with Noise Models](https://mlsanity.com/paper/2402.16514/) (2024)
- [Detecting 3D Line Segments for 6DoF Pose Estimation with Limited Data](https://mlsanity.com/paper/2601.12090/) (2026)

## Abstract (arXiv)

An automated robotic system needs to be as robust as possible and fail-safe in general while having relatively high precision and repeatability. Although deep learning-based methods are becoming research standard on how to approach 3D scan and image processing tasks, the industry standard for processing this data is still analytically-based. Our paper claims that analytical methods are less robust and harder for testing, updating, and maintaining. This paper focuses on a specific task of 6D pose estimation of a bin in 3D scans. Therefore, we present a high-quality dataset composed of synthetic data and real scans captured by a structured-light scanner with precise annotations. Additionally, we propose two different methods for 6D bin pose estimation, an analytical method as the industrial standard and a baseline data-driven method. Both approaches are cross-evaluated, and our experiments show that augmenting the training on real scans with synthetic data improves our proposed data-driven neural model. This position paper is preliminary, as proposed methods are trained and evaluated on a relatively small initial dataset which we plan to extend in the future.