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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.15088 |
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| _version_ | 1866913129879830528 |
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| author | Bekar, Batuhan Arda Sarı, Can Gülkan, Hüseyin Can Özcan, Barış |
| author_facet | Bekar, Batuhan Arda Sarı, Can Gülkan, Hüseyin Can Özcan, Barış |
| contents | We present SAGE3D, a hybrid Transformer-based model for corner detection in airborne LiDAR point clouds. We propose a multi-stage solution built on a hierarchical encoder-decoder architecture that progressively downsamples point clouds through Set Abstraction layers and recovers per-point predictions via Feature Propagation. We introduce two innovations: Soft-Guided Attention, which injects ground-truth corner labels as a log-prior into attention logits during training to improve precision; then an Excitatory Graph Neural Network positioned at strategic resolutions in the hierarchy, employing positive-only message passing where high-confidence corners reinforce predictions through learned boosting, optimizing for recall. The hierarchical design enables multi-scale feature extraction while our guided attention and excitatory modules ensure corner signals are amplified rather than diluted across scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15088 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SAGE3D: Soft-guided attention and graph excitation for 3D point cloud corner detection Bekar, Batuhan Arda Sarı, Can Gülkan, Hüseyin Can Özcan, Barış Computer Vision and Pattern Recognition We present SAGE3D, a hybrid Transformer-based model for corner detection in airborne LiDAR point clouds. We propose a multi-stage solution built on a hierarchical encoder-decoder architecture that progressively downsamples point clouds through Set Abstraction layers and recovers per-point predictions via Feature Propagation. We introduce two innovations: Soft-Guided Attention, which injects ground-truth corner labels as a log-prior into attention logits during training to improve precision; then an Excitatory Graph Neural Network positioned at strategic resolutions in the hierarchy, employing positive-only message passing where high-confidence corners reinforce predictions through learned boosting, optimizing for recall. The hierarchical design enables multi-scale feature extraction while our guided attention and excitatory modules ensure corner signals are amplified rather than diluted across scales. |
| title | SAGE3D: Soft-guided attention and graph excitation for 3D point cloud corner detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.15088 |