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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.2975711
|2 doi
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|a pubmed24n1308.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Xu, Zhengtian
|e verfasserin
|4 aut
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| 245 |
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|a Outdoor RGBD Instance Segmentation with Residual Regretting Learning
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Indoor semantic segmentation with RGBD input has received decent progress recently, but studies on instance-level objects in outdoor scenarios meet challenges due to the ambiguity in the acquired outdoor depth map. To tackle this problem, we proposed a residual regretting mechanism, incorporated into current flexible, general and solid instance segmentation framework Mask R-CNN in an end-to-end manner. Specifically, regretting cascade is designed to gradually refine and fully unearth useful information in depth maps, acting in a filtering and backup way. Additionally, embedded by a novel residual connection structure, the regretting module combines RGB and depth branches with pixel-level mask robustly. Extensive experiments on the challenging Cityscapes and KITTI dataset manifest the effectiveness of our residual regretting scheme for handling outdoor depth map. Our approach achieves state-of-the-art performance on RGBD instance segmentation, with 13.4% relative improvement over Mask R-CNN on Cityscapes by depth cue
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|a Journal Article
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|a Liu, Shu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shi, Jianping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lu, Cewu
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2020) vom: 27. Feb.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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|g day:27
|g month:02
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|u http://dx.doi.org/10.1109/TIP.2020.2975711
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