# Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing

arXiv: https://arxiv.org/abs/1903.04713 · HTML: https://mlsanity.com/paper/1903.04713/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning](https://mlsanity.com/paper/2204.02863/) (2022)
- [One-Shot Dual-Arm Imitation Learning](https://mlsanity.com/paper/2503.06831/) (2025)
- [In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components](https://mlsanity.com/paper/2208.01284/) (2022)
- [CNSv2: Probabilistic Correspondence Encoded Neural Image Servo](https://mlsanity.com/paper/2503.00132/) (2025)
- [Self-supervised Wide Baseline Visual Servoing via 3D Equivariance](https://mlsanity.com/paper/2209.05432/) (2022)
- [A Closed-Loop Multi-perspective Visual Servoing Approach with Reinforcement Learning](https://mlsanity.com/paper/2312.15809/) (2023)
- [Imitation Learning-based Direct Visual Servoing using the Large Projection Formulation](https://mlsanity.com/paper/2406.09120/) (2024)
- [Landscape-Awareness for Geometric View Diffusion Model](https://mlsanity.com/paper/2605.19865/) (2026)
- [DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy](https://mlsanity.com/paper/2606.19397/) (2026)
- [WM-VS: Progress-Aligned World Models for Closed-Loop Visual Servoing](https://mlsanity.com/paper/2609.20892/) (2026)
- [VGM-VS: Rethinking Visual Geometry Model for High-Precision Visual Servoing](https://mlsanity.com/paper/2609.28312/) (2026)

## Abstract (arXiv)

Visual Servoing (VS), where images taken from a camera typically attached to the robot end-effector are used to guide the robot motions, is an important technique to tackle robotic tasks that require a high level of accuracy. We propose a new neural network, based on a Siamese architecture, for highly accurate camera pose estimation. This, in turn, can be used as a final refinement step following a coarse VS or, if applied in an iterative manner, as a standalone VS on its own. The key feature of our neural network is that it outputs the relative pose between any pair of images, and does so with sub-millimeter accuracy. We show that our network can reduce pose estimation errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial errors of 10 mm / 5 degrees if applied once, or of several cm / tens of degrees if applied iteratively. The network can generalize to similar objects, is robust against changing lighting conditions, and to partial occlusions (when used iteratively). The high accuracy achieved enables tackling low-tolerance assembly tasks downstream: using our network, an industrial robot can achieve 97.5% success rate on a VGA-connector insertion task without any force sensing mechanism.