Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Article Swipe
Shogo Sato
,
Yasuhiro Yao
,
Taiga Yoshida
,
Takuhiro Kaneko
,
Shingo Ando
,
Jun Shimamura
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.10820
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2303.10820
Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization of LiDAR, especially LiDAR intensity, to address this issue. In this paper, we propose unsupervised intrinsic image decomposition with LiDAR intensity (IID-LI). Since the conventional unsupervised learning methods consist of image-to-image transformations, simply inputting LiDAR intensity is not an effective approach. Therefore, we design an intensity consistency loss that computes the error between LiDAR intensity and gray-scaled albedo to provide a criterion for the ill-posed problem. In addition, LiDAR intensity is difficult to handle due to its sparsity and occlusion, hence, a LiDAR intensity densification module is proposed. We verified the estimating quality using our own dataset, which include RGB images, LiDAR intensity and human judged annotations. As a result, we achieved an estimation accuracy that outperforms conventional unsupervised learning methods. Dataset link : (https://github.com/ntthilab-cv/NTT-intrinsic-dataset).
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2303.10820
- https://arxiv.org/pdf/2303.10820
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4353007058
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- OpenAlex ID
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https://openalex.org/W4353007058Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2303.10820Digital Object Identifier
- Title
-
Unsupervised Intrinsic Image Decomposition with LiDAR IntensityWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-20Full publication date if available
- Authors
-
Shogo Sato, Yasuhiro Yao, Taiga Yoshida, Takuhiro Kaneko, Shingo Ando, Jun ShimamuraList of authors in order
- Landing page
-
https://arxiv.org/abs/2303.10820Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2303.10820Direct 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/2303.10820Direct OA link when available
- Concepts
-
Lidar, Computer science, Artificial intelligence, Ranging, Remote sensing, Ground truth, Unsupervised learning, Computer vision, Albedo (alchemy), Pattern recognition (psychology), Geography, Art, Performance art, Art history, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.methods, | 24 |
| abstract_inverted_index.methods. | 209 |
| abstract_inverted_index.problem. | 154 |
| abstract_inverted_index.sparsity | 166 |
| abstract_inverted_index.verified | 178 |
| abstract_inverted_index.(IID-LI). | 110 |
| abstract_inverted_index.Intrinsic | 0 |
| abstract_inverted_index.Recently, | 63 |
| abstract_inverted_index.addition, | 156 |
| abstract_inverted_index.approach. | 129 |
| abstract_inverted_index.criterion | 150 |
| abstract_inverted_index.currently | 48 |
| abstract_inverted_index.detection | 65 |
| abstract_inverted_index.difficult | 160 |
| abstract_inverted_index.effective | 128 |
| abstract_inverted_index.ill-posed | 61, 153 |
| abstract_inverted_index.inputting | 122 |
| abstract_inverted_index.intensity | 109, 124, 134, 143, 158, 172, 191 |
| abstract_inverted_index.intrinsic | 104 |
| abstract_inverted_index.observing | 34 |
| abstract_inverted_index.problems. | 62 |
| abstract_inverted_index.proposed. | 176 |
| abstract_inverted_index.typically | 19 |
| abstract_inverted_index.Therefore, | 130 |
| abstract_inverted_index.decomposes | 8 |
| abstract_inverted_index.difficulty | 32 |
| abstract_inverted_index.especially | 91 |
| abstract_inverted_index.estimating | 180 |
| abstract_inverted_index.estimation | 202 |
| abstract_inverted_index.intensity, | 93 |
| abstract_inverted_index.occlusion, | 168 |
| abstract_inverted_index.supervised | 22, 50 |
| abstract_inverted_index.Conversely, | 43 |
| abstract_inverted_index.consistency | 135 |
| abstract_inverted_index.gray-scaled | 145 |
| abstract_inverted_index.outperforms | 205 |
| abstract_inverted_index.utilization | 88 |
| abstract_inverted_index.annotations. | 195 |
| abstract_inverted_index.conventional | 113, 206 |
| abstract_inverted_index.unsupervised | 44, 103, 114, 207 |
| abstract_inverted_index.decomposition | 2, 106 |
| abstract_inverted_index.densification | 173 |
| abstract_inverted_index.measurements. | 81 |
| abstract_inverted_index.image-to-image | 119 |
| abstract_inverted_index.underperforming | 49 |
| abstract_inverted_index.transformations, | 120 |
| abstract_inverted_index.(https://github.com/ntthilab-cv/NTT-intrinsic-dataset). | 213 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.5400000214576721 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |