PAPR: Proximity Attention Point Rendering Article Swipe
Yanshu Zhang
,
Shichong Peng
,
Alireza Moazeni
,
Ke Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.11086
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.11086
Learning accurate and parsimonious point cloud representations of scene surfaces from scratch remains a challenge in 3D representation learning. Existing point-based methods often suffer from the vanishing gradient problem or require a large number of points to accurately model scene geometry and texture. To address these limitations, we propose Proximity Attention Point Rendering (PAPR), a novel method that consists of a point-based scene representation and a differentiable renderer. Our scene representation uses a point cloud where each point is characterized by its spatial position, influence score, and view-independent feature vector. The renderer selects the relevant points for each ray and produces accurate colours using their associated features. PAPR effectively learns point cloud positions to represent the correct scene geometry, even when the initialization drastically differs from the target geometry. Notably, our method captures fine texture details while using only a parsimonious set of points. We also demonstrate four practical applications of our method: zero-shot geometry editing, object manipulation, texture transfer, and exposure control. More results and code are available on our project website at https://zvict.github.io/papr/.
Related Topics
Concepts
Point cloud
Rendering (computer graphics)
Computer science
Initialization
Artificial intelligence
Computer vision
Point (geometry)
Differentiable function
Computer graphics (images)
Representation (politics)
Geometry
Mathematics
Law
Politics
Mathematical analysis
Programming language
Political science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.11086
- https://arxiv.org/pdf/2307.11086
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385009370
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385009370Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.11086Digital Object Identifier
- Title
-
PAPR: Proximity Attention Point RenderingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-20Full publication date if available
- Authors
-
Yanshu Zhang, Shichong Peng, Alireza Moazeni, Ke LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.11086Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.11086Direct 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/2307.11086Direct OA link when available
- Concepts
-
Point cloud, Rendering (computer graphics), Computer science, Initialization, Artificial intelligence, Computer vision, Point (geometry), Differentiable function, Computer graphics (images), Representation (politics), Geometry, Mathematics, Law, Politics, Mathematical analysis, Programming language, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.challenge | 14 |
| abstract_inverted_index.features. | 106 |
| abstract_inverted_index.geometry, | 118 |
| abstract_inverted_index.geometry. | 128 |
| abstract_inverted_index.influence | 84 |
| abstract_inverted_index.learning. | 18 |
| abstract_inverted_index.position, | 83 |
| abstract_inverted_index.positions | 112 |
| abstract_inverted_index.practical | 148 |
| abstract_inverted_index.renderer. | 67 |
| abstract_inverted_index.represent | 114 |
| abstract_inverted_index.transfer, | 159 |
| abstract_inverted_index.vanishing | 26 |
| abstract_inverted_index.zero-shot | 153 |
| abstract_inverted_index.accurately | 37 |
| abstract_inverted_index.associated | 105 |
| abstract_inverted_index.demonstrate | 146 |
| abstract_inverted_index.drastically | 123 |
| abstract_inverted_index.effectively | 108 |
| abstract_inverted_index.point-based | 20, 61 |
| abstract_inverted_index.applications | 149 |
| abstract_inverted_index.limitations, | 46 |
| abstract_inverted_index.parsimonious | 3, 140 |
| abstract_inverted_index.characterized | 79 |
| abstract_inverted_index.manipulation, | 157 |
| abstract_inverted_index.differentiable | 66 |
| abstract_inverted_index.initialization | 122 |
| abstract_inverted_index.representation | 17, 63, 70 |
| abstract_inverted_index.representations | 6 |
| abstract_inverted_index.view-independent | 87 |
| abstract_inverted_index.https://zvict.github.io/papr/. | 174 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |