# SilhoNet: An RGB Method for 6D Object Pose Estimation

arXiv: https://arxiv.org/abs/1809.06893 · HTML: https://mlsanity.com/paper/1809.06893/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 14 of 14)

- [PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation](https://mlsanity.com/paper/2211.14125/) (2022)
- [Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation](https://mlsanity.com/paper/2303.11516/) (2023)
- [Video based Object 6D Pose Estimation using Transformers](https://mlsanity.com/paper/2210.13540/) (2022)
- [Uni6Dv2: Noise Elimination for 6D Pose Estimation](https://mlsanity.com/paper/2208.06416/) (2022)
- [Model-Based Underwater 6D Pose Estimation from RGB](https://mlsanity.com/paper/2302.06821/) (2023)
- [HandyPriors: Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors](https://mlsanity.com/paper/2311.16552/) (2023)
- [CASAPose: Class-Adaptive and Semantic-Aware Multi-Object Pose Estimation](https://mlsanity.com/paper/2210.05318/) (2022)
- [VideoPose: Estimating 6D object pose from videos](https://mlsanity.com/paper/2111.10677/) (2021)
- [MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters](https://mlsanity.com/paper/2310.05762/) (2023)
- [Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation](https://mlsanity.com/paper/2405.10557/) (2024)
- [Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding](https://mlsanity.com/paper/2212.01920/) (2022)
- [FastPoseCNN: Real-Time Monocular Category-Level Pose and Size Estimation Framework](https://mlsanity.com/paper/2406.11063/) (2024)
- [SEMPose: A Single End-to-end Network for Multi-object Pose Estimation](https://mlsanity.com/paper/2411.14002/) (2024)
- [MVTOP: Multi-View Transformer-based Object Pose-Estimation](https://mlsanity.com/paper/2508.03243/) (2025)

## Abstract (arXiv)

Autonomous robot manipulation involves estimating the translation and orientation of the object to be manipulated as a 6-degree-of-freedom (6D) pose. Methods using RGB-D data have shown great success in solving this problem. However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors. When limited to monocular camera data only, the problem of object pose estimation is very challenging. In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images. We use a Convolutional Neural Network (CNN) pipeline that takes in Region of Interest (ROI) proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask and a 3D translation vector. The 3D orientation is then regressed from the predicted silhouettes. We show that our method achieves better overall performance on the YCB-Video dataset than two state-of-the art networks for 6D pose estimation from monocular image input.