Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State
Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8020 |
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| author | Mena Nagiub Thorsten Beuth Ganesh Sistu Heinrich Gotzig Ciarán Eising |
| author_facet | Mena Nagiub Thorsten Beuth Ganesh Sistu Heinrich Gotzig Ciarán Eising |
| author_sort | Mena Nagiub |
| collection | DOAJ |
| description | Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods. Additionally, blind deconvolution is introduced to mitigate the blur. The combined use of these methods produces high-quality depth maps with reduced blur noise. |
| format | Article |
| id | doaj-art-a3054931fe6a403b8fba9fec8d5331e4 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a3054931fe6a403b8fba9fec8d5331e42024-12-27T14:52:44ZengMDPI AGSensors1424-82202024-12-012424802010.3390/s24248020Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion StateMena Nagiub0Thorsten Beuth1Ganesh Sistu2Heinrich Gotzig3Ciarán Eising4Department of Front Camera, Valeo Schalter und Sensoren GmbH, 74321 Bietigheim-Bissingen, GermanyDepartment of Detection Systems, Valeo Detection Systems GmbH, 74321 Bietigheim-Bissingen, GermanyDepartment of Electronic & Computer Engineer, University of Limerick, V94 T9PX Limerick, IrelandDeptartment of Driving Assistance, Valeo Schalter und Sensoren GmbH, 74321 Bietigheim-Bissingen, GermanyDepartment of Electronic & Computer Engineer, University of Limerick, V94 T9PX Limerick, IrelandCurrent deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods. Additionally, blind deconvolution is introduced to mitigate the blur. The combined use of these methods produces high-quality depth maps with reduced blur noise.https://www.mdpi.com/1424-8220/24/24/8020near fieldLidariTOFdepth correctionestimationambiguity |
| spellingShingle | Mena Nagiub Thorsten Beuth Ganesh Sistu Heinrich Gotzig Ciarán Eising Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State Sensors near field Lidar iTOF depth correction estimation ambiguity |
| title | Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State |
| title_full | Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State |
| title_fullStr | Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State |
| title_full_unstemmed | Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State |
| title_short | Depth Prediction Improvement for Near-Field iToF Lidar in Low-Speed Motion State |
| title_sort | depth prediction improvement for near field itof lidar in low speed motion state |
| topic | near field Lidar iTOF depth correction estimation ambiguity |
| url | https://www.mdpi.com/1424-8220/24/24/8020 |
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