# Probabilistic 3D Multilabel Real-time Mapping for Multi-object Manipulation

arXiv: https://arxiv.org/abs/2001.05752 · HTML: https://mlsanity.com/paper/2001.05752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

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

- [Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation](https://mlsanity.com/paper/2106.12534/) (2021)
- [Exploiting Radiance Fields for Grasp Generation on Novel Synthetic Views](https://mlsanity.com/paper/2505.11467/) (2025)

## Abstract (arXiv)

Probabilistic 3D map has been applied to object segmentation with multiple camera viewpoints, however, conventional methods lack of real-time efficiency and functionality of multilabel object mapping. In this paper, we propose a method to generate three-dimensional map with multilabel occupancy in real-time. Extending our previous work in which only target label occupancy is mapped, we achieve multilabel object segmentation in a single looking around action. We evaluate our method by testing segmentation accuracy with 39 different objects, and applying it to a manipulation task of multiple objects in the experiments. Our mapping-based method outperforms the conventional projection-based method by 40 - 96% relative (12.6 mean IU3d), and robot successfully recognizes (86.9%) and manipulates multiple objects (60.7%) in an environment with heavy occlusions.