# Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

arXiv: https://arxiv.org/abs/1809.08564 · HTML: https://mlsanity.com/paper/1809.08564/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenes](https://mlsanity.com/paper/2410.23004/) (2024)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibility](https://mlsanity.com/paper/2503.12609/) (2025)
- [Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains](https://mlsanity.com/paper/2106.01866/) (2021)
- [Active-Perceptive Motion Generation for Mobile Manipulation](https://mlsanity.com/paper/2310.00433/) (2023)
- [Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenes](https://mlsanity.com/paper/2101.04781/) (2021)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior](https://mlsanity.com/paper/2402.15402/) (2024)
- [Observe Then Act: Asynchronous Active Vision-Action Model for Robotic Manipulation](https://mlsanity.com/paper/2409.14891/) (2024)
- [Optimizing Active Perception for Learning Simultaneous Viewpoint Selection and Manipulation with Diffusion Policy](https://mlsanity.com/paper/2409.14615/) (2024)
- [Learning to Look: Seeking Information for Decision Making via Policy Factorization](https://mlsanity.com/paper/2410.18964/) (2024)
- [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025)
- [A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions](https://mlsanity.com/paper/2209.07459/) (2022)
- [DNRSelect: Active Best View Selection for Deferred Neural Rendering](https://mlsanity.com/paper/2501.12150/) (2025)
- [Robustness of Robotic Manipulation: Foundations and Frontiers](https://mlsanity.com/paper/2606.31494/) (2026)

## Abstract (arXiv)

Camera viewpoint selection is an important aspect of visual grasp detection, especially in clutter where many occlusions are present. Where other approaches use a static camera position or fixed data collection routines, our Multi-View Picking (MVP) controller uses an active perception approach to choose informative viewpoints based directly on a distribution of grasp pose estimates in real time, reducing uncertainty in the grasp poses caused by clutter and occlusions. In trials of grasping 20 objects from clutter, our MVP controller achieves 80% grasp success, outperforming a single-viewpoint grasp detector by 12%. We also show that our approach is both more accurate and more efficient than approaches which consider multiple fixed viewpoints.