# Towards Explaining Uncertainty Estimates in Point Cloud Registration

arXiv: https://arxiv.org/abs/2412.20612 · HTML: https://mlsanity.com/paper/2412.20612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 27 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [TARGO: Benchmarking Target-driven Object Grasping under Occlusions](https://mlsanity.com/paper/2407.06168/) (2024)

## Built on by (most cited first, 1 of 1)

- [Human-Interpretable Uncertainty Explanations for Point Cloud Registration](https://mlsanity.com/paper/2509.18786/) (2025)

## Abstract (arXiv)

Iterative Closest Point (ICP) is a commonly used algorithm to estimate transformation between two point clouds. The key idea of this work is to leverage recent advances in explainable AI for probabilistic ICP methods that provide uncertainty estimates. Concretely, we propose a method that can explain why a probabilistic ICP method produced a particular output. Our method is based on kernel SHAP (SHapley Additive exPlanations). With this, we assign an importance value to common sources of uncertainty in ICP such as sensor noise, occlusion, and ambiguous environments. The results of the experiment show that this explanation method can reasonably explain the uncertainty sources, providing a step towards robots that know when and why they failed in a human interpretable manner