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Large-Scale ALS Point Cloud Segmentation via Projection-Based Context Embedding
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DOI：10.1109/TGRS.2024.3392267.png)
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Abstract
En 中文
Semantic segmentation of airborne laser scanning (ALS) point clouds is a valuable yet challenging task in remote sensing. When processing large-scale ALS scenes, it is necessary to partition them into smaller blocks for ease of handling. However, this partitioning introduces a challenge in capturing the ample spatial context within each block to adequately recognize the objects with a significant spatial span. This limitation becomes particularly pronounced when relying solely on the 3-D representations as the input of neural networks. To incorporate sufficient contextual information in ALS data semantic segmentation, we propose a multimodal-based segmentation framework called projection-based context embedding (PCE) in this study. PCE effectively combines the advantages of 2-D image and 3-D point-voxel representations, which are the computational efficiency and the representation capability for fine-grained 3-D geometries. The 2-D projection is used to encode a large-scale semantic context, which is computationally expensive to be obtained using only pure 3-D representation. Simultaneously, the sparse-point-voxel convolution (SPVConv) is employed to focus on learning 3-D features from a small block of points centered on the large-scale context. Finally, to fully exploit the power of each modality, the embedding disentangling (ED) strategy is proposed additionally to combine the context embedding from the 2-D image with 3-D features for the final prediction. We demonstrate the state-of-the-art performance of PCE through extensive experiments on public large-scale ALS point cloud datasets.
Keywords:
Three-dimensional displays
Point cloud compression
Semantic segmentation
Feature extraction
Semantics
Computational efficiency
Lasers
Airborne laser scanning (ALS)
contextual information
point cloud
semantic segmentation
Journal
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8.6
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2.1W
Citations:
10.7W