# Center Direction Network for Grasping Point Localization on Cloths

arXiv: https://arxiv.org/abs/2408.14456 · HTML: https://mlsanity.com/paper/2408.14456/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 15 of 15)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework](https://mlsanity.com/paper/2107.12746/) (2021)
- [Point-Query Quadtree for Crowd Counting, Localization, and More](https://mlsanity.com/paper/2308.13814/) (2023)
- [Cloth Region Segmentation for Robust Grasp Selection](https://mlsanity.com/paper/2008.05626/) (2020)
- [Learning Keypoints for Robotic Cloth Manipulation using Synthetic Data](https://mlsanity.com/paper/2401.01734/) (2024)
- [CenterNet++ for Object Detection](https://mlsanity.com/paper/2204.08394/) (2022)
- [Household Cloth Object Set: Fostering Benchmarking in Deformable Object Manipulation](https://mlsanity.com/paper/2111.01527/) (2021)
- [Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision](https://mlsanity.com/paper/2110.02903/) (2021)
- [Learning Keypoints from Synthetic Data for Robotic Cloth Folding](https://mlsanity.com/paper/2205.06714/) (2022)
- [Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation](https://mlsanity.com/paper/2305.03259/) (2023)

## Built on by (most cited first, 2 of 2)

- [SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation](https://mlsanity.com/paper/2409.18082/) (2024)
- [Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding](https://mlsanity.com/paper/2609.31452/) (2026)

## Abstract (arXiv)

Object grasping is a fundamental challenge in robotics and computer vision, critical for advancing robotic manipulation capabilities. Deformable objects, like fabrics and cloths, pose additional challenges due to their non-rigid nature. In this work, we introduce CeDiRNet-3DoF, a deep-learning model for grasp point detection, with a particular focus on cloth objects. CeDiRNet-3DoF employs center direction regression alongside a localization network, attaining first place in the perception task of ICRA 2023's Cloth Manipulation Challenge. Recognizing the lack of standardized benchmarks in the literature that hinder effective method comparison, we present the ViCoS Towel Dataset. This extensive benchmark dataset comprises 8,000 real and 12,000 synthetic images, serving as a robust resource for training and evaluating contemporary data-driven deep-learning approaches. Extensive evaluation revealed CeDiRNet-3DoF's robustness in real-world performance, outperforming state-of-the-art methods, including the latest transformer-based models. Our work bridges a crucial gap, offering a robust solution and benchmark for cloth grasping in computer vision and robotics. Code and dataset are available at: https://github.com/vicoslab/CeDiRNet-3DoF