# Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images

arXiv: https://arxiv.org/abs/1809.10699 · HTML: https://mlsanity.com/paper/1809.10699/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [3D Part Assembly Generation with Instance Encoded Transformer](https://mlsanity.com/paper/2207.01779/) (2022)
- [Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing](https://mlsanity.com/paper/2402.17464/) (2024)
- [Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation](https://mlsanity.com/paper/2504.06961/) (2025)
- [6D Robotic Assembly Based on RGB-only Object Pose Estimation](https://mlsanity.com/paper/2208.12986/) (2022)
- [Learning Eye-in-Hand Camera Calibration from a Single Image](https://mlsanity.com/paper/2111.01245/) (2021)
- [Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly](https://mlsanity.com/paper/2110.04450/) (2021)
- [Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities](https://mlsanity.com/paper/2107.05789/) (2021)
- [DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis](https://mlsanity.com/paper/2107.13087/) (2021)
- [Generalizable Pose Estimation Using Implicit Scene Representations](https://mlsanity.com/paper/2305.17252/) (2023)
- [LocaliseBot: Multi-view 3D object localisation with differentiable rendering for robot grasping](https://mlsanity.com/paper/2311.08438/) (2023)
- [Zero-Shot Peg Insertion: Identifying Mating Holes and Estimating SE(2) Poses with Vision-Language Models](https://mlsanity.com/paper/2503.06026/) (2025)

## Abstract (arXiv)

Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new tasks. In this work we aim towards flexible and adaptable robotic assembly by using 3D CAD models for all parts to be assembled. We focus on a generic assembly task - the Siemens Innovation Challenge - in which a robot needs to assemble a gear-like mechanism with high precision into an operating system. To obtain the millimeter-accuracy required for this task and industrial settings alike, we use a depth camera mounted near the robot end-effector. We present a high-accuracy two-stage pose estimation procedure based on deep convolutional neural networks, which includes detection, pose estimation, refinement, and handling of near- and full symmetries of parts. The networks are trained on simulated depth images with means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.16 millimeters and 0.64 degree leading to 91% success rate for robotic assembly of randomly distributed parts. To the best of our knowledge, this is the first time that the Siemens Innovation Challenge is fully addressed, with all the parts assembled with high success rates.