# ROI-based Robotic Grasp Detection for Object Overlapping Scenes

arXiv: https://arxiv.org/abs/1808.10313 · HTML: https://mlsanity.com/paper/1808.10313/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 30 of 31)

- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [A Joint Network for Grasp Detection Conditioned on Natural Language Commands](https://mlsanity.com/paper/2104.00492/) (2021)
- [Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions](https://mlsanity.com/paper/2205.04028/) (2022)
- [Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding](https://mlsanity.com/paper/2301.11564/) (2023)
- [Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance](https://mlsanity.com/paper/2407.13842/) (2024)
- [MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis](https://mlsanity.com/paper/2208.03963/) (2022)
- [ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping](https://mlsanity.com/paper/2504.10857/) (2025)
- [When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection](https://mlsanity.com/paper/2202.11911/) (2022)
- [REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter](https://mlsanity.com/paper/2104.14118/) (2021)
- [DA2 Dataset: Toward Dexterity-Aware Dual-Arm Grasping](https://mlsanity.com/paper/2208.00408/) (2022)
- [RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention](https://mlsanity.com/paper/2302.14264/) (2023)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketches](https://mlsanity.com/paper/2205.04026/) (2022)
- [Constrained 6-DoF Grasp Generation on Complex Shapes for Improved Dual-Arm Manipulation](https://mlsanity.com/paper/2404.04643/) (2024)
- [Double-Dot Network for Antipodal Grasp Detection](https://mlsanity.com/paper/2108.01527/) (2021)
- [MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis](https://mlsanity.com/paper/2112.14663/) (2021)
- [A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions](https://mlsanity.com/paper/2209.07459/) (2022)
- [Language-driven Grasp Detection with Mask-guided Attention](https://mlsanity.com/paper/2407.19877/) (2024)
- [Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection](https://mlsanity.com/paper/2101.10226/) (2021)
- [SynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes](https://mlsanity.com/paper/2307.07333/) (2023)
- [FViT-Grasp: Grasping Objects With Using Fast Vision Transformers](https://mlsanity.com/paper/2311.13986/) (2023)
- [Visual Affordance Prediction: Survey and Reproducibility](https://mlsanity.com/paper/2505.05074/) (2025)
- [RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation](https://mlsanity.com/paper/2603.14880/) (2026)
- [Real-World Semantic Grasp Detection Based on Attention Mechanism](https://mlsanity.com/paper/2111.10522/) (2021)
- [A novel integrated method of detection-grasping for specific object based on the box coordinate matching](https://mlsanity.com/paper/2307.11783/) (2023)
- [Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images](https://mlsanity.com/paper/2310.19223/) (2023)
- [Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments](https://mlsanity.com/paper/2507.05861/) (2025)
- [SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes](https://mlsanity.com/paper/2508.20547/) (2025)

## Abstract (arXiv)

Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the robotic grasping approach. In this paper, a robotic grasp detection algorithm named ROI-GD is proposed to provide a feasible solution to this problem based on Region of Interest (ROI), which is the region proposal for objects. ROI-GD uses features from ROIs to detect grasps instead of the whole scene. It has two stages: the first stage is to provide ROIs in the input image and the second-stage is the grasp detector based on ROI features. We also contribute a multi-object grasp dataset, which is much larger than Cornell Grasp Dataset, by labeling Visual Manipulation Relationship Dataset. Experimental results demonstrate that ROI-GD performs much better in object overlapping scenes and at the meantime, remains comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. Robotic experiments demonstrate that ROI-GD can help robots grasp the target in single-object and multi-object scenes with the overall success rates of 92.5% and 83.8% respectively.