# A metric for evaluating 3D reconstruction and mapping performance with no ground truthing

arXiv: https://arxiv.org/abs/2101.10402 · HTML: https://mlsanity.com/paper/2101.10402/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [RGBD Datasets: Past, Present and Future](https://mlsanity.com/paper/1604.00999/) (2016)
- [LoopSmart: Smart Visual SLAM Through Surface Loop Closure](https://mlsanity.com/paper/1801.01572/) (2018)

## Built on by (most cited first, 4 of 4)

- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection](https://mlsanity.com/paper/2211.07748/) (2022)
- [Self-optimizing loop sifting and majorization for 3D reconstruction](https://mlsanity.com/paper/2104.10826/) (2021)
- [Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction](https://mlsanity.com/paper/2510.08839/) (2025)

## Abstract (arXiv)

It is not easy when evaluating 3D mapping performance because existing metrics require ground truth data that can only be collected with special instruments. In this paper, we propose a metric, dense map posterior (DMP), for this evaluation. It can work without any ground truth data. Instead, it calculates a comparable value, reflecting a map posterior probability, from dense point cloud observations. In our experiments, the proposed DMP is benchmarked against ground truth-based metrics. Results show that DMP can provide a similar evaluation capability. The proposed metric makes evaluating different methods more flexible and opens many new possibilities, such as self-supervised methods and more available datasets.