TransLoc3D

KO | EN

gitlite — search

#javascript #react #ai #llm #golang #go #machine-learning #deep-learning #linux #docker #macos #awesome #awesome-list #ios

(★ 19)

TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields (CVM2022)

TransLoc3D 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# TransLoc3D : Point Cloud based Large-scale Place Recognition using Adaptive Receptive Fields

Paper URL: [https://arxiv.org/abs/2105.11605](https://arxiv.org/abs/2105.11605)

## Abstract

Place recognition plays an essential role in the field of autonomous driving and robot navigation. Although a number of point cloud based methods have been proposed and achieved promising results, few of them take the size difference of objects into consideration. For small objects like pedestrians and vehicles, large receptive fields will capture unrelated information, while small receptive fields would fail to encode complete geometric information for large objects such as buildings. We argue that fixed receptive fields are not well suited for place recognition, and propose a novel Adaptive Receptive Field Module (ARFM), which can adaptively adjust the size of the receptive field based on the input point cloud. We also present a novel network architecture, named TransLoc3D, to obtain discriminative global descriptors of point clouds for the place recognition task. TransLoc3D consists of a 3D sparse convolutional module, an ARFM module, an external transformer network which aims to capture long range dependency and a NetVLAD layer. Experiments show that our method outperforms prior state-of-the-art results, with an improvement of 1.1\% on average recall@1 on the Oxford RobotCar dataset, and 0.8\% on the B.D. dataset.

## Results on Baseline Dataset

| Network          | AR@1     | AR@1\%   |
| ---------------- | -------- | -------- |
| PointNetVLAD     | 63.3     | 80.3     |
| PCAN             | 70.7     | 83.8     |
| DAGC             | 73.3     | 87.5     |
| LPD-Net          | 86.3     | 94.9     |
| SOE-Net          | 89.4     | 96.4     |
| Minkloc3D        | 93.8     | 97.9     |
| NDT-Transformer  | 93.8     | 97.7     |
| Minkloc++        | 93.9     | 98.2     |
| TransLoc3D(ours) | **95.0** | **98.5** |

## Acknowledgments

We would like to sincerely thank [Minkloc3D](https://github.com/jac99/MinkLoc3D), [Minkloc++](https://github.com/jac99/MinkLocMultimodal), [mmdetection](https://github.com/open-mmlab/mmdetection) for their awesome released code.

TransLoc3D

TransLoc3D 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

TransLoc3D

TransLoc3D 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README