ZeroShape: Regression-based Zero-shot Shape Reconstruction Article Swipe
Zixuan Huang
,
Stefan Stojanov
,
Anh Thai
,
Varun Jampani
,
James M. Rehg
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.14198
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.14198
We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models are computationally expensive at train and inference time. In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance, or conversely, are regression-based approaches still competitive? To answer this, we design a strong regression-based model, called ZeroShape, based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark, with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models, aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods, but also demonstrates significantly higher computational and data efficiency.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.14198
- https://arxiv.org/pdf/2312.14198
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390214187
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390214187Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.14198Digital Object Identifier
- Title
-
ZeroShape: Regression-based Zero-shot Shape ReconstructionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-21Full publication date if available
- Authors
-
Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. RehgList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.14198Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.14198Direct 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/2312.14198Direct OA link when available
- Concepts
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Benchmark (surveying), Computer science, Regression, Artificial intelligence, Inference, Machine learning, Generative grammar, Generative model, Field (mathematics), Contrast (vision), Regression analysis, Algorithm, Pattern recognition (psychology), Mathematics, Statistics, Geodesy, Geography, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.problem | 3, 40 |
| abstract_inverted_index.regress | 50 |
| abstract_inverted_index.through | 16 |
| abstract_inverted_index.trained | 47 |
| abstract_inverted_index.achieves | 160 |
| abstract_inverted_index.approach | 37 |
| abstract_inverted_index.directly | 49 |
| abstract_inverted_index.evaluate | 142 |
| abstract_inverted_index.findings | 99 |
| abstract_inverted_index.insight. | 106 |
| abstract_inverted_index.methods, | 165 |
| abstract_inverted_index.methods. | 64 |
| abstract_inverted_index.modeling | 18, 72 |
| abstract_inverted_index.reducing | 147 |
| abstract_inverted_index.superior | 161 |
| abstract_inverted_index.variance | 150 |
| abstract_inverted_index.ZeroShape | 157 |
| abstract_inverted_index.benchmark | 125 |
| abstract_inverted_index.contrast, | 34 |
| abstract_inverted_index.datasets. | 122 |
| abstract_inverted_index.different | 119 |
| abstract_inverted_index.expensive | 27 |
| abstract_inverted_index.inference | 31 |
| abstract_inverted_index.magnitude | 133 |
| abstract_inverted_index.necessary | 73 |
| abstract_inverted_index.question: | 69 |
| abstract_inverted_index.zero-shot | 6, 13 |
| abstract_inverted_index.ZeroShape, | 94 |
| abstract_inverted_index.approaches | 81 |
| abstract_inverted_index.benchmark, | 114 |
| abstract_inverted_index.converging | 98 |
| abstract_inverted_index.efficiency | 61 |
| abstract_inverted_index.evaluation | 113, 124, 149 |
| abstract_inverted_index.generative | 17, 63, 71 |
| abstract_inverted_index.real-world | 112, 120 |
| abstract_inverted_index.regression | 55 |
| abstract_inverted_index.conversely, | 78 |
| abstract_inverted_index.efficiency. | 174 |
| abstract_inverted_index.performance | 162 |
| abstract_inverted_index.traditional | 36 |
| abstract_inverted_index.competitive? | 83 |
| abstract_inverted_index.demonstrates | 168 |
| abstract_inverted_index.performance, | 76 |
| abstract_inverted_index.single-image | 5 |
| abstract_inverted_index.computational | 60, 171 |
| abstract_inverted_index.deterministic | 44 |
| abstract_inverted_index.significantly | 169 |
| abstract_inverted_index.quantitatively | 141 |
| abstract_inverted_index.reconstruction | 15 |
| abstract_inverted_index.computationally | 26 |
| abstract_inverted_index.reconstruction. | 9 |
| abstract_inverted_index.regression-based | 80, 91 |
| abstract_inverted_index.state-of-the-art | 164 |
| abstract_inverted_index.regression-based, | 42 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |