Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework Article Swipe
Xu Ma
,
Can Qin
,
Haoxuan You
,
Haoxi Ran
,
Yun Fu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.07123
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2202.07123
Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors using convolution, graph, or attention mechanisms. These methods, however, incur unfavorable latency during inference, and the performance saturates over the past few years. In this paper, we present a novel perspective on this task. We notice that detailed local geometrical information probably is not the key to point cloud analysis -- we introduce a pure residual MLP network, called PointMLP, which integrates no sophisticated local geometrical extractors but still performs very competitively. Equipped with a proposed lightweight geometric affine module, PointMLP delivers the new state-of-the-art on multiple datasets. On the real-world ScanObjectNN dataset, our method even surpasses the prior best method by 3.3% accuracy. We emphasize that PointMLP achieves this strong performance without any sophisticated operations, hence leading to a superior inference speed. Compared to most recent CurveNet, PointMLP trains 2x faster, tests 7x faster, and is more accurate on ModelNet40 benchmark. We hope our PointMLP may help the community towards a better understanding of point cloud analysis. The code is available at https://github.com/ma-xu/pointMLP-pytorch.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2202.07123
- https://arxiv.org/pdf/2202.07123
- OA Status
- green
- Cited By
- 303
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221160819
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221160819Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2202.07123Digital Object Identifier
- Title
-
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP FrameworkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-02-15Full publication date if available
- Authors
-
Xu Ma, Can Qin, Haoxuan You, Haoxi Ran, Yun FuList of authors in order
- Landing page
-
https://arxiv.org/abs/2202.07123Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2202.07123Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2202.07123Direct OA link when available
- Concepts
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Point cloud, Computer science, Residual, Inference, Affine transformation, Cloud computing, Benchmark (surveying), Sketch, Key (lock), Graph, Point (geometry), Theoretical computer science, Artificial intelligence, Algorithm, Geometry, Mathematics, Geodesy, Geography, Operating system, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
303Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 49, 2024: 112, 2023: 118, 2022: 24Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.incur | 36 |
| abstract_inverted_index.local | 24, 65, 91 |
| abstract_inverted_index.novel | 56 |
| abstract_inverted_index.point | 74, 182 |
| abstract_inverted_index.prior | 17, 125 |
| abstract_inverted_index.still | 95 |
| abstract_inverted_index.task. | 60 |
| abstract_inverted_index.tests | 159 |
| abstract_inverted_index.using | 27 |
| abstract_inverted_index.which | 87 |
| abstract_inverted_index.works | 18 |
| abstract_inverted_index.affine | 105 |
| abstract_inverted_index.better | 179 |
| abstract_inverted_index.called | 85 |
| abstract_inverted_index.during | 39 |
| abstract_inverted_index.graph, | 29 |
| abstract_inverted_index.mainly | 19 |
| abstract_inverted_index.method | 121, 127 |
| abstract_inverted_index.notice | 62 |
| abstract_inverted_index.paper, | 52 |
| abstract_inverted_index.recent | 153 |
| abstract_inverted_index.speed. | 149 |
| abstract_inverted_index.strong | 137 |
| abstract_inverted_index.trains | 156 |
| abstract_inverted_index.years. | 49 |
| abstract_inverted_index.capture | 13 |
| abstract_inverted_index.faster, | 158, 161 |
| abstract_inverted_index.latency | 38 |
| abstract_inverted_index.leading | 144 |
| abstract_inverted_index.module, | 106 |
| abstract_inverted_index.present | 54 |
| abstract_inverted_index.towards | 177 |
| abstract_inverted_index.without | 139 |
| abstract_inverted_index.Compared | 150 |
| abstract_inverted_index.Equipped | 99 |
| abstract_inverted_index.PointMLP | 107, 134, 155, 172 |
| abstract_inverted_index.accurate | 165 |
| abstract_inverted_index.achieves | 135 |
| abstract_inverted_index.analysis | 2, 76 |
| abstract_inverted_index.dataset, | 119 |
| abstract_inverted_index.delivers | 108 |
| abstract_inverted_index.detailed | 64 |
| abstract_inverted_index.however, | 35 |
| abstract_inverted_index.methods, | 34 |
| abstract_inverted_index.multiple | 113 |
| abstract_inverted_index.network, | 84 |
| abstract_inverted_index.performs | 96 |
| abstract_inverted_index.probably | 68 |
| abstract_inverted_index.proposed | 102 |
| abstract_inverted_index.residual | 82 |
| abstract_inverted_index.superior | 147 |
| abstract_inverted_index.CurveNet, | 154 |
| abstract_inverted_index.PointMLP, | 86 |
| abstract_inverted_index.accuracy. | 130 |
| abstract_inverted_index.analysis. | 184 |
| abstract_inverted_index.attention | 31 |
| abstract_inverted_index.available | 188 |
| abstract_inverted_index.community | 176 |
| abstract_inverted_index.datasets. | 114 |
| abstract_inverted_index.emphasize | 132 |
| abstract_inverted_index.exploring | 22 |
| abstract_inverted_index.geometric | 25, 104 |
| abstract_inverted_index.inference | 148 |
| abstract_inverted_index.introduce | 79 |
| abstract_inverted_index.saturates | 44 |
| abstract_inverted_index.surpasses | 123 |
| abstract_inverted_index.unordered | 9 |
| abstract_inverted_index.ModelNet40 | 167 |
| abstract_inverted_index.benchmark. | 168 |
| abstract_inverted_index.extractors | 26, 93 |
| abstract_inverted_index.inference, | 40 |
| abstract_inverted_index.integrates | 88 |
| abstract_inverted_index.real-world | 117 |
| abstract_inverted_index.structure. | 11 |
| abstract_inverted_index.challenging | 4 |
| abstract_inverted_index.geometrical | 66, 92 |
| abstract_inverted_index.geometries, | 16 |
| abstract_inverted_index.information | 67 |
| abstract_inverted_index.lightweight | 103 |
| abstract_inverted_index.mechanisms. | 32 |
| abstract_inverted_index.operations, | 142 |
| abstract_inverted_index.performance | 43, 138 |
| abstract_inverted_index.perspective | 57 |
| abstract_inverted_index.unfavorable | 37 |
| abstract_inverted_index.ScanObjectNN | 118 |
| abstract_inverted_index.convolution, | 28 |
| abstract_inverted_index.irregularity | 7 |
| abstract_inverted_index.sophisticated | 23, 90, 141 |
| abstract_inverted_index.understanding | 180 |
| abstract_inverted_index.competitively. | 98 |
| abstract_inverted_index.state-of-the-art | 111 |
| abstract_inverted_index.https://github.com/ma-xu/pointMLP-pytorch. | 190 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |