CASSC: Context‐aware method for depth guided semantic scene completion
Abstract Semantic scene completion is a crucial end‐to‐end 3D perception task, and the 3D information perception subjects is vital for autonomous driving. This paper presents CASSC, a novel adaptive context‐aware method based on Transformer networks, aimed at realizing camera‐based semantic scene co...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.13280 |
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| _version_ | 1850118349429669888 |
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| author | Jinghao Cao Ming Li Sheng Liu Yang Li Sidan Du |
| author_facet | Jinghao Cao Ming Li Sheng Liu Yang Li Sidan Du |
| author_sort | Jinghao Cao |
| collection | DOAJ |
| description | Abstract Semantic scene completion is a crucial end‐to‐end 3D perception task, and the 3D information perception subjects is vital for autonomous driving. This paper presents CASSC, a novel adaptive context‐aware method based on Transformer networks, aimed at realizing camera‐based semantic scene completion algorithms. The key idea is to leverage rich context information from images to obtain pixel‐level label proposals, followed by designing a multiscale fusion mechanism to merge this information and match it with voxel space. A weakly supervised training strategy is proposed to obtain semantic label distribution features from images and introduce an adaptive multiscale fusion module to fuse and adaptively match these features with voxel space. Here, CASSC achieves state‐of‐the‐art performance on the SemanticKITTI dataset and demonstrates excellent performance on the SSC‐Bench dataset. Ablation experiments validate the rationality and effectiveness of our design, and the model and code of CASSC will be open‐sourced on https://github.com/dogooooo/CASSC. |
| format | Article |
| id | doaj-art-c7baabe01d664fd0888a9a4456b5ecbf |
| institution | OA Journals |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-c7baabe01d664fd0888a9a4456b5ecbf2025-08-20T02:35:53ZengWileyIET Image Processing1751-96591751-96672024-12-0118144716473010.1049/ipr2.13280CASSC: Context‐aware method for depth guided semantic scene completionJinghao Cao0Ming Li1Sheng Liu2Yang Li3Sidan Du4School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu ChinaSchool of Electronic Science and Engineering Nanjing University Nanjing Jiangsu ChinaSchool of Electronic Science and Engineering Nanjing University Nanjing Jiangsu ChinaSchool of Electronic Science and Engineering Nanjing University Nanjing Jiangsu ChinaSchool of Electronic Science and Engineering Nanjing University Nanjing Jiangsu ChinaAbstract Semantic scene completion is a crucial end‐to‐end 3D perception task, and the 3D information perception subjects is vital for autonomous driving. This paper presents CASSC, a novel adaptive context‐aware method based on Transformer networks, aimed at realizing camera‐based semantic scene completion algorithms. The key idea is to leverage rich context information from images to obtain pixel‐level label proposals, followed by designing a multiscale fusion mechanism to merge this information and match it with voxel space. A weakly supervised training strategy is proposed to obtain semantic label distribution features from images and introduce an adaptive multiscale fusion module to fuse and adaptively match these features with voxel space. Here, CASSC achieves state‐of‐the‐art performance on the SemanticKITTI dataset and demonstrates excellent performance on the SSC‐Bench dataset. Ablation experiments validate the rationality and effectiveness of our design, and the model and code of CASSC will be open‐sourced on https://github.com/dogooooo/CASSC.https://doi.org/10.1049/ipr2.13280computer visionconvolutional neural netsimage processingsupervised learning |
| spellingShingle | Jinghao Cao Ming Li Sheng Liu Yang Li Sidan Du CASSC: Context‐aware method for depth guided semantic scene completion IET Image Processing computer vision convolutional neural nets image processing supervised learning |
| title | CASSC: Context‐aware method for depth guided semantic scene completion |
| title_full | CASSC: Context‐aware method for depth guided semantic scene completion |
| title_fullStr | CASSC: Context‐aware method for depth guided semantic scene completion |
| title_full_unstemmed | CASSC: Context‐aware method for depth guided semantic scene completion |
| title_short | CASSC: Context‐aware method for depth guided semantic scene completion |
| title_sort | cassc context aware method for depth guided semantic scene completion |
| topic | computer vision convolutional neural nets image processing supervised learning |
| url | https://doi.org/10.1049/ipr2.13280 |
| work_keys_str_mv | AT jinghaocao cassccontextawaremethodfordepthguidedsemanticscenecompletion AT mingli cassccontextawaremethodfordepthguidedsemanticscenecompletion AT shengliu cassccontextawaremethodfordepthguidedsemanticscenecompletion AT yangli cassccontextawaremethodfordepthguidedsemanticscenecompletion AT sidandu cassccontextawaremethodfordepthguidedsemanticscenecompletion |