A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910875772780544 |
|---|---|
| author | Deng, Wenbang Chen, Xieyuanli Yu, Qinghua He, Yunze Xiao, Junhao Lu, Huimin |
| author_facet | Deng, Wenbang Chen, Xieyuanli Yu, Qinghua He, Yunze Xiao, Junhao Lu, Huimin |
| contents | Semantic segmentation is a key technique that enables mobile robots to understand and navigate surrounding environments autonomously. However, most existing works focus on segmenting known objects, overlooking the identification of unknown classes, which is common in real-world applications. In this paper, we propose a feature-oriented framework for open-set semantic segmentation on LiDAR data, capable of identifying unknown objects while retaining the ability to classify known ones. We design a decomposed dual-decoder network to simultaneously perform closed-set semantic segmentation and generate distinctive features for unknown objects. The network is trained with multi-objective loss functions to capture the characteristics of known and unknown objects. Using the extracted features, we introduce an anomaly detection mechanism to identify unknown objects. By integrating the results of close-set semantic segmentation and anomaly detection, we achieve effective feature-driven LiDAR open-set semantic segmentation. Evaluations on both SemanticKITTI and nuScenes datasets demonstrate that our proposed framework significantly outperforms state-of-the-art methods. The source code will be made publicly available at https://github.com/nubot-nudt/DOSS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data Deng, Wenbang Chen, Xieyuanli Yu, Qinghua He, Yunze Xiao, Junhao Lu, Huimin Computer Vision and Pattern Recognition Semantic segmentation is a key technique that enables mobile robots to understand and navigate surrounding environments autonomously. However, most existing works focus on segmenting known objects, overlooking the identification of unknown classes, which is common in real-world applications. In this paper, we propose a feature-oriented framework for open-set semantic segmentation on LiDAR data, capable of identifying unknown objects while retaining the ability to classify known ones. We design a decomposed dual-decoder network to simultaneously perform closed-set semantic segmentation and generate distinctive features for unknown objects. The network is trained with multi-objective loss functions to capture the characteristics of known and unknown objects. Using the extracted features, we introduce an anomaly detection mechanism to identify unknown objects. By integrating the results of close-set semantic segmentation and anomaly detection, we achieve effective feature-driven LiDAR open-set semantic segmentation. Evaluations on both SemanticKITTI and nuScenes datasets demonstrate that our proposed framework significantly outperforms state-of-the-art methods. The source code will be made publicly available at https://github.com/nubot-nudt/DOSS. |
| title | A Novel Decomposed Feature-Oriented Framework for Open-Set Semantic Segmentation on LiDAR Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.11097 |