NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

Zhou, Zhicheng; Zhao, Cheng; Adolfsson, Daniel; Su, Songzhi; Gao, Yang; Duckett, Tom; Sun, Li

doi:10.48550/arxiv.2103.12292

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“…Leveraging the representation prowess of Transformers for point cloud analysis is still under-explored. Along this nascent direction, encouraging results have started to emerge [37], [38]. However, the complexity of data, especially in the outdoor domain, has still not allowed researchers to exploit the key strength of Transformers, i.e., multi-head attention, in their techniques.…”

Section: Related Workmentioning

confidence: 99%

Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps

Ibrahim¹,

Akhtar²,

Anwar³

et al. 2023

Preprint

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Section: Related Workmentioning

confidence: 99%

Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps

Ibrahim¹,

Akhtar²,

Anwar³

et al. 2023

Preprint

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

Guo

Lai

et al. 2021

Preprint

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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.

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NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

Cited by 2 publications

References 23 publications

Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps

Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps

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

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