# 3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-Art

arXiv: https://arxiv.org/abs/1802.02297 · HTML: https://mlsanity.com/paper/1802.02297/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning](https://mlsanity.com/paper/2103.14533/) (2021)
- [A Graph-Matching Approach for Cross-view Registration of Over-view 2 and Street-view based Point Clouds](https://mlsanity.com/paper/2202.06857/) (2022)
- [SO(3)-Pose: SO(3)-Equivariance Learning for 6D Object Pose Estimation](https://mlsanity.com/paper/2208.08338/) (2022)
- [Composite Convolution: a Flexible Operator for Deep Learning on 3D Point Clouds](https://mlsanity.com/paper/2209.11796/) (2022)
- [Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping](https://mlsanity.com/paper/2109.00900/) (2021)
- [3D Point Cloud Registration with Learning-based Matching Algorithm](https://mlsanity.com/paper/2202.02149/) (2022)

## Abstract (arXiv)

The introduction of inexpensive 3D data acquisition devices has promisingly facilitated the wide availability and popularity of 3D point cloud, which attracts more attention to the effective extraction of novel 3D point cloud descriptors for accuracy of the efficiency of 3D computer vision tasks in recent years. However, how to develop discriminative and robust feature descriptors from 3D point cloud remains a challenging task due to their intrinsic characteristics. In this paper, we give a comprehensively insightful investigation of the existing 3D point cloud descriptors. These methods can principally be divided into two categories according to the advancement of descriptors: hand-crafted based and deep learning-based apporaches, which will be further discussed from the perspective of elaborate classification, their advantages, and limitations. Finally, we present the future research direction of the extraction of 3D point cloud descriptors.