# MVGrasp: Real-Time Multi-View 3D Object Grasping in Highly Cluttered Environments

arXiv: https://arxiv.org/abs/2103.10997 · HTML: https://mlsanity.com/paper/2103.10997/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2021.

## Built on by (most cited first, 10 of 10)

- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Meta-Learning Regrasping Strategies for Physical-Agnostic Objects](https://mlsanity.com/paper/2205.11110/) (2022)
- [Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition](https://mlsanity.com/paper/2205.01982/) (2022)
- [Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks](https://mlsanity.com/paper/2109.11544/) (2021)
- [Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything](https://mlsanity.com/paper/2412.03472/) (2024)
- [Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models](https://mlsanity.com/paper/2210.04613/) (2022)
- [A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping](https://mlsanity.com/paper/2406.11786/) (2024)
- [GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping](https://mlsanity.com/paper/2210.03628/) (2022)
- [LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition](https://mlsanity.com/paper/2504.19256/) (2025)
- [Learning to Throw Objects Safely in Multi-Obstacle Environments](https://mlsanity.com/paper/2607.06388/) (2026)

## Abstract (arXiv)

Nowadays robots play an increasingly important role in our daily life. In human-centered environments, robots often encounter piles of objects, packed items, or isolated objects. Therefore, a robot must be able to grasp and manipulate different objects in various situations to help humans with daily tasks. In this paper, we propose a multi-view deep learning approach to handle robust object grasping in human-centric domains. In particular, our approach takes a point cloud of an arbitrary object as an input, and then, generates orthographic views of the given object. The obtained views are finally used to estimate pixel-wise grasp synthesis for each object. We train the model end-to-end using a small object grasp dataset and test it on both simulations and real-world data without any further fine-tuning. To evaluate the performance of the proposed approach, we performed extensive sets of experiments in three scenarios, including isolated objects, packed items, and pile of objects. Experimental results show that our approach performed very well in all simulation and real-robot scenarios, and is able to achieve reliable closed-loop grasping of novel objects across various scene configurations.