# RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images

arXiv: https://arxiv.org/abs/2103.02184 · HTML: https://mlsanity.com/paper/2103.02184/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 42 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://mlsanity.com/paper/1812.00324/) (2018)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019)
- [Understanding Convolution for Semantic Segmentation](https://mlsanity.com/paper/1702.08502/) (2017)
- [Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation](https://mlsanity.com/paper/1806.08756/) (2018)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://mlsanity.com/paper/1803.11469/) (2018)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/1910.14218/) (2019)
- [Robotic Grasp Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1611.08036/) (2016)
- [PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds](https://mlsanity.com/paper/2003.09644/) (2020)
- [Robust Grasp Planning Over Uncertain Shape Completions](https://mlsanity.com/paper/1903.00645/) (2019)
- [DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-based Robotic Grasping](https://mlsanity.com/paper/2001.05279/) (2020)
- [Densely Supervised Grasp Detector (DSGD)](https://mlsanity.com/paper/1810.03962/) (2018)
- [Estimating 6D Pose From Localizing Designated Surface Keypoints](https://mlsanity.com/paper/1812.01387/) (2018)
- [Transferring Grasp Configurations using Active Learning and Local Replanning](https://mlsanity.com/paper/1807.08341/) (2018)

## Built on by (most cited first, 30 of 39)

- [DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation](https://mlsanity.com/paper/2210.02697/) (2022)
- [UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy](https://mlsanity.com/paper/2303.00938/) (2023)
- [UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning](https://mlsanity.com/paper/2304.00464/) (2023)
- [GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRF](https://mlsanity.com/paper/2210.06575/) (2022)
- [TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline](https://mlsanity.com/paper/2202.08471/) (2022)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning](https://mlsanity.com/paper/2402.02500/) (2024)
- [PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations](https://mlsanity.com/paper/2303.16958/) (2023)
- [SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping](https://mlsanity.com/paper/2103.12311/) (2021)
- [SPA: 3D Spatial-Awareness Enables Effective Embodied Representation](https://mlsanity.com/paper/2410.08208/) (2024)
- [An Economic Framework for 6-DoF Grasp Detection](https://mlsanity.com/paper/2407.08366/) (2024)
- [Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2212.05275/) (2022)
- [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)
- [CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation](https://mlsanity.com/paper/2312.08240/) (2023)
- [Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes](https://mlsanity.com/paper/2403.18546/) (2024)
- [Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input](https://mlsanity.com/paper/2209.08752/) (2022)
- [ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera](https://mlsanity.com/paper/2405.05648/) (2024)
- [HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models](https://mlsanity.com/paper/2409.10419/) (2024)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects](https://mlsanity.com/paper/2312.01307/) (2023)
- [Hybrid Physical Metric For 6-DoF Grasp Pose Detection](https://mlsanity.com/paper/2206.11141/) (2022)
- [KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input](https://mlsanity.com/paper/2303.05617/) (2023)
- [GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes](https://mlsanity.com/paper/2312.03345/) (2023)
- [Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation](https://mlsanity.com/paper/2403.11511/) (2024)
- [Articulated Object Manipulation using Online Axis Estimation with SAM2-Based Tracking](https://mlsanity.com/paper/2409.16287/) (2024)
- [ADPro: a Test-time Adaptive Diffusion Policy via Manifold-constrained Denoising and Task-aware Initialization for Robotic Manipulation](https://mlsanity.com/paper/2508.06266/) (2025)
- [Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction](https://mlsanity.com/paper/2305.09510/) (2023)
- [You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects](https://mlsanity.com/paper/2404.03462/) (2024)
- [Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection](https://mlsanity.com/paper/2407.15771/) (2024)

## Abstract (arXiv)

General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most of the success grasps, or only take the unstable depth image or point cloud as input which may lead to poor results in some cases. In this paper, we propose RGBD-Grasp, a pipeline that solves this problem by decoupling 7-DoF grasp detection into two sub-tasks where RGB and depth information are processed separately. In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image. Consequently, a Fast Analytic Searching(FAS) module calculates the opening width and the distance of the gripper to the grasp point. By decoupling the grasp detection problem and introducing the stable RGB modality, our pipeline alleviates the requirement for the high-quality depth image and is robust to depth sensor noise. We achieve state-of-the-art results on GraspNet-1Billion dataset compared with several baselines. Real robot experiments on a UR5 robot with an Intel Realsense camera and a Robotiq two-finger gripper show high success rates for both single object scenes and cluttered scenes. Our code and trained model will be made publicly available.