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UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models paper
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Paper

UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models

00955036ff547cee2bea6e47ff540cadf64469d6
Free2AITools Nexus Index
69.2
S: Semantic 50

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A: Authority 85
P: Popularity 61
R: Recency 100
Q: Quality 65
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In classical computer vision, rectification is an integral part of multi-view depth estimation. It typically includes epipolar rectification and lens distortion correction. This process simplifies the depth estimation significantly, and thus it has been adopted in CNN approaches. However, rectification has several side effects, including a reduced field of view (FOV), resampling distortion, and sensitivity to calibration errors. The effects are particularly pronounced in case of significant d...

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Registry ID 00955036ff547cee2bea6e47ff540cadf64469d6
License ArXiv
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BibTeX 
@misc{00955036ff547cee2bea6e47ff540cadf64469d6,
  title = {UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models Paper},
  howpublished = {\url{https://api.semanticscholar.org/00955036ff547cee2bea6e47ff540cadf64469d6}},
  note = {Accessed via Free2AITools.}
}
APA Style
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models [Paper]. Free2AITools. https://api.semanticscholar.org/00955036ff547cee2bea6e47ff540cadf64469d6

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Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 85
Popularity (P) 61
Recency (R) 100
Quality (Q) 65

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FNI V2.0 for UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models: Authority (A:85), Popularity (P:61), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"In classical computer vision, rectification is an integral part of multi-view depth estimation. It typically includes epipolar rectification and lens distortion correction. This process simplifies the depth estimation significantly, and thus it has been adopted in CNN approaches. However, rectification has several side effects, including a reduced field of view (FOV), resampling distortion, and sensitivity to calibration errors. The effects are particularly pronounced in case of significant d..."

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@article{UnRectDepthNet,
  title={UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models},
  note={Indexed by Free2AITools}
}

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πŸ“ˆ60CitationsSemantic Scholar
πŸ›οΈ85AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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