Open-world semantic segmentation for lidar point clouds

Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e. The closed-set assumption makes the network only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what it has seen. Therefore, in this work, we propose the open-world semantic segmentation task for LIDAR point clouds, which aims to 1 identify both old and novel classes using open-set semantic segmentation, and 2 gradually incorporate novel objects into the existing knowledge base using incremental learning without forgetting old classes. For this purpose, we propose a RE dund A ncy c L open-world semantic segmentation for lidar point clouds REAL framework to provide a general architecture for both the open-set semantic segmentation and incremental learning problems.

However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation. The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis. In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations of previous work.

Open-world semantic segmentation for lidar point clouds

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Chapter Google Scholar. Gal, Y.

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Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e. The closed-set assumption makes the network only able to output labels of trained classes, even for objects never seen before, while a static network cannot update its knowledge base according to what it has seen. Therefore, in this work, we propose the open-world semantic segmentation task for LIDAR point clouds, which aims to 1 identify both old and novel classes using open-set semantic segmentation, and 2 gradually incorporate novel objects into the existing knowledge base using incremental learning without forgetting old classes. For this purpose, we propose a RE dund A ncy c L assifier REAL framework to provide a general architecture for both the open-set semantic segmentation and incremental learning problems. The experimental results show that REAL can simultaneously achieves state-of-the-art performance in the open-set semantic segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the catastrophic forgetting problem with a large margin during incremental learning.

Open-world semantic segmentation for lidar point clouds

Open-world Semantic Segmentati Incremental learning. LIDAR point clouds. Open-set semantic segmentation.

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Rights and permissions Reprints and permissions. For this purpose, we propose a RE dund A ncy c L assifier REAL framework to provide a general architecture for both the open-set semantic segmentation and incremental learning problems. Baur, C. Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation. The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis. Wu, B. In this paper, we introduce a comprehensive 3D pre-training framework designed to facilitate the acquisition of efficient 3D representations, thereby establishing a pathway to 3D foundational models. Point clouds are unstructured and unordered data, as opposed to images. Read previous issues. Description with markdown optional :. Data evaluated on. In: IROS In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations of previous work.

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The experimental results show that REAL can simultaneously achieves state-of-the-art performance in the open-set semantic segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the catastrophic forgetting problem with a large margin during incremental learning. Sorry, a shareable link is not currently available for this article. Hendrycks, D. Delange, M. Liu, L. You can create a new account if you don't have one. Online ISBN : Most implemented papers Most implemented Social Latest No code. Google Scholar. Peng, C. Caesar, H. Reprints and permissions. Yun, P. ULS labeled data.