AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation Article Swipe
Yuanwen Yue
,
Sabarinath Mahadevan
,
Jonas Schult
,
Francis Engelmann
,
Bastian Leibe
,
Konrad Schindler
,
Theodora Kontogianni
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.00977
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.00977
During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model. The current best practice formulates the problem as binary classification and segments objects one at a time. The model expects the user to provide positive clicks to indicate regions wrongly assigned to the background and negative clicks on regions wrongly assigned to the object. Sequentially visiting objects is wasteful since it disregards synergies between objects: a positive click for a given object can, by definition, serve as a negative click for nearby objects. Moreover, a direct competition between adjacent objects can speed up the identification of their common boundary. We introduce AGILE3D, an efficient, attention-based model that (1) supports simultaneous segmentation of multiple 3D objects, (2) yields more accurate segmentation masks with fewer user clicks, and (3) offers faster inference. Our core idea is to encode user clicks as spatial-temporal queries and enable explicit interactions between click queries as well as between them and the 3D scene through a click attention module. Every time new clicks are added, we only need to run a lightweight decoder that produces updated segmentation masks. In experiments with four different 3D point cloud datasets, AGILE3D sets a new state-of-the-art. Moreover, we also verify its practicality in real-world setups with real user studies.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.00977
- https://arxiv.org/pdf/2306.00977
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379261324
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379261324Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.00977Digital Object Identifier
- Title
-
AGILE3D: Attention Guided Interactive Multi-object 3D SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-01Full publication date if available
- Authors
-
Yuanwen Yue, Sabarinath Mahadevan, Jonas Schult, Francis Engelmann, Bastian Leibe, Konrad Schindler, Theodora KontogianniList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.00977Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.00977Direct 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/2306.00977Direct OA link when available
- Concepts
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Computer science, Segmentation, Object (grammar), Point cloud, Inference, Point (geometry), Artificial intelligence, Process (computing), Computer vision, Boundary (topology), Operating system, Mathematics, Mathematical analysis, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.yields | 158 |
| abstract_inverted_index.AGILE3D | 232 |
| abstract_inverted_index.assigns | 26 |
| abstract_inverted_index.between | 105, 129, 187, 193 |
| abstract_inverted_index.clicks, | 166 |
| abstract_inverted_index.current | 53 |
| abstract_inverted_index.decoder | 217 |
| abstract_inverted_index.expects | 71 |
| abstract_inverted_index.module. | 203 |
| abstract_inverted_index.object. | 95 |
| abstract_inverted_index.objects | 12, 64, 98, 131 |
| abstract_inverted_index.problem | 58 |
| abstract_inverted_index.provide | 75 |
| abstract_inverted_index.queries | 182, 189 |
| abstract_inverted_index.regions | 80, 90 |
| abstract_inverted_index.through | 199 |
| abstract_inverted_index.updated | 220 |
| abstract_inverted_index.wrongly | 81, 91 |
| abstract_inverted_index.AGILE3D, | 143 |
| abstract_inverted_index.accurate | 160 |
| abstract_inverted_index.adjacent | 130 |
| abstract_inverted_index.assigned | 82, 92 |
| abstract_inverted_index.corrects | 39 |
| abstract_inverted_index.explicit | 185 |
| abstract_inverted_index.indicate | 79 |
| abstract_inverted_index.interest | 14 |
| abstract_inverted_index.multiple | 154 |
| abstract_inverted_index.negative | 87, 120 |
| abstract_inverted_index.objects, | 156 |
| abstract_inverted_index.objects. | 124 |
| abstract_inverted_index.objects: | 106 |
| abstract_inverted_index.positive | 76, 108 |
| abstract_inverted_index.practice | 55 |
| abstract_inverted_index.process, | 23 |
| abstract_inverted_index.produces | 219 |
| abstract_inverted_index.segments | 63 |
| abstract_inverted_index.studies. | 249 |
| abstract_inverted_index.supports | 150 |
| abstract_inverted_index.together | 9 |
| abstract_inverted_index.visiting | 97 |
| abstract_inverted_index.wasteful | 100 |
| abstract_inverted_index.Moreover, | 125, 237 |
| abstract_inverted_index.attention | 202 |
| abstract_inverted_index.boundary. | 140 |
| abstract_inverted_index.datasets, | 231 |
| abstract_inverted_index.delineate | 11 |
| abstract_inverted_index.different | 227 |
| abstract_inverted_index.introduce | 142 |
| abstract_inverted_index.iterative | 22 |
| abstract_inverted_index.resulting | 43 |
| abstract_inverted_index.synergies | 104 |
| abstract_inverted_index.background | 85 |
| abstract_inverted_index.disregards | 103 |
| abstract_inverted_index.efficient, | 145 |
| abstract_inverted_index.formulates | 56 |
| abstract_inverted_index.inference. | 171 |
| abstract_inverted_index.real-world | 244 |
| abstract_inverted_index.competition | 128 |
| abstract_inverted_index.definition, | 116 |
| abstract_inverted_index.experiments | 224 |
| abstract_inverted_index.interactive | 1 |
| abstract_inverted_index.lightweight | 216 |
| abstract_inverted_index.Sequentially | 96 |
| abstract_inverted_index.background), | 35 |
| abstract_inverted_index.interactions | 186 |
| abstract_inverted_index.practicality | 242 |
| abstract_inverted_index.segmentation | 44, 152, 161, 221 |
| abstract_inverted_index.simultaneous | 151 |
| abstract_inverted_index.segmentation, | 2 |
| abstract_inverted_index.classification | 61 |
| abstract_inverted_index.identification | 136 |
| abstract_inverted_index.attention-based | 146 |
| abstract_inverted_index.spatial-temporal | 181 |
| abstract_inverted_index.state-of-the-art. | 236 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |