# V-PRISM: Probabilistic Mapping of Unknown Tabletop Scenes

arXiv: https://arxiv.org/abs/2403.08106 · HTML: https://mlsanity.com/paper/2403.08106/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2024.

## Built on by (most cited first, 9 of 9)

- [RecGS: Removing Water Caustic with Recurrent Gaussian Splatting](https://mlsanity.com/paper/2407.10318/) (2024)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [SplaTraj: Camera Trajectory Generation with Semantic Gaussian Splatting](https://mlsanity.com/paper/2410.06014/) (2024)
- [3D Foundation Models Enable Simultaneous Geometry and Pose Estimation of Grasped Objects](https://mlsanity.com/paper/2407.10331/) (2024)
- [GraphSeg: Segmented 3D Representations via Graph Edge Addition and Contraction](https://mlsanity.com/paper/2504.03129/) (2025)
- [Unifying Scene Representation and Hand-Eye Calibration with 3D Foundation Models](https://mlsanity.com/paper/2404.11683/) (2024)
- [Robust Bayesian Scene Reconstruction with Retrieval-Augmented Priors for Precise Grasping and Planning](https://mlsanity.com/paper/2411.19461/) (2024)
- [Efficient Construction of Implicit Surface Models From a Single Image for Motion Generation](https://mlsanity.com/paper/2509.20681/) (2025)
- [ContraMap: Contrastive Uncertainty Mapping for Robot Environment Representation](https://mlsanity.com/paper/2603.27632/) (2026)

## Abstract (arXiv)

The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGB-D image. These representations are then critical for a range of downstream manipulation tasks. Many previous attempts to tackle this problem do not capture accurate uncertainty, which is required to subsequently produce safe motion plans. In this paper, we cast the representation of 3D tabletop scenes as a multi-class classification problem. To tackle this, we introduce V-PRISM, a framework and method for robustly creating probabilistic 3D segmentation maps of tabletop scenes. Our maps contain both occupancy estimates, segmentation information, and principled uncertainty measures. We evaluate the robustness of our method in (1) procedurally generated scenes using open-source object datasets, and (2) real-world tabletop data collected from a depth camera. Our experiments show that our approach outperforms alternative continuous reconstruction approaches that do not explicitly reason about objects in a multi-class formulation.