Open-world semantic segmentation for lidar point clouds

Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e.

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. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. 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.

Open-world semantic segmentation for lidar point clouds

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Bendale, A. Firstly, a frustum feature encoder module is used to extract per-point features within the frustum region, which preserves scene consistency and is crucial for point-level predictions.

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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. This is a preview of subscription content, log in via an institution. Baur, C. Google Scholar. Behley, J. In: ICCV

Open-world semantic segmentation for lidar point clouds

Open-world Semantic Segmentati Incremental learning. LIDAR point clouds.

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Bozhinoski, D. IEEE Robot. Or, discuss a change on Slack. Policies and ethics. Download references. About this paper. Description with markdown optional :. Springer, Cham. 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. Li, D. In: 3DV Milioto, A. You can create a new account if you don't have one.

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Read previous issues. Computer Vision. About this paper. Li, Z. Cen, J. Higher is better for the metric. 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. Bendale, A. You can create a new account if you don't have one. ECCV In The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis. Springer, Cham.