# Detection, Recognition and Pose Estimation of Tabletop Objects

arXiv: https://arxiv.org/abs/2409.00869 · HTML: https://mlsanity.com/paper/2409.00869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge](https://mlsanity.com/paper/1609.09475/) (2016)
- [Learning Descriptors for Object Recognition and 3D Pose Estimation](https://mlsanity.com/paper/1502.05908/) (2015)
- [Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation](https://mlsanity.com/paper/1607.06038/) (2016)

## Abstract (arXiv)

The problem of cleaning a messy table using Deep Neural Networks is a very interesting problem in both social and industrial robotics. This project focuses on the social application of this technology. A neural network model that is capable of detecting and recognizing common tabletop objects, such as a mug, mouse, or stapler is developed. The model also predicts the angle at which these objects are placed on a table,with respect to some reference. Assuming each object has a fixed intended position and orientation on the tabletop, the orientation of a particular object predicted by the deep learning model can be used to compute the transformation matrix to move the object from its initial position to the intended position. This can be fed to a pick and place robot to carry out the transfer.This paper talks about the deep learning approaches used in this project for object detection and orientation estimation.