# Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching

## TL;DR

This paper comprehensively evaluates nine state-of-the-art local geometric feature representations for 3D data matching, considering various datasets, application scenarios, and perturbations, providing insights into their robustness and effectiveness.

## Contribution

It introduces a thorough evaluation framework leveraging ground-truth LRFs to compare feature representations across multiple datasets and scenarios, filling a gap in existing research.

## Key findings

- Certain descriptors show high robustness to noise and occlusion.
- Distinctiveness varies significantly among evaluated methods.
- The evaluation offers guidance for selecting features in real-world applications.

## Abstract

Local geometric descriptors remain an essential component for 3D rigid data matching and fusion. The devise of a rotational invariant local geometric descriptor usually consists of two steps: local reference frame (LRF) construction and feature representation. Existing evaluation efforts have mainly been paid on the LRF or the overall descriptor, yet the quantitative comparison of feature representations remains unexplored. This paper fills this gap by comprehensively evaluating nine state-of-the-art local geometric feature representations. Our evaluation is on the ground that ground-truth LRFs are leveraged such that the ranking of tested feature representations are more convincing as opposed to existing studies. The experiments are deployed on six standard datasets with various application scenarios (shape retrieval, point cloud registration, and object recognition) and data modalities (LiDAR, Kinect, and Space Time) as well as perturbations including Gaussian noise, shot noise, data decimation, clutter, occlusion, and limited overlap. The evaluated terms cover the major concerns for a feature representation, e.g., distinctiveness, robustness, compactness, and efficiency. The outcomes present interesting findings that may shed new light on this community and provide complementary perspectives to existing evaluations on the topic of local geometric feature description. A summary of evaluated methods regarding their peculiarities is also presented to guide real-world applications and new descriptor crafting.