DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation

arXiv:2510.10471 · cs.CV, cs.LG · Submitted 2025-10-12 · Read on arXiv

cs.CV, cs.LG

Submitted: 2025-10-12

Updated: 2026-09-05

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Point Clouds — 29× in this paper · explained in 2 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Feature Extraction — 16× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 11× in this paper
• CVF International Conference on Computer Vision — 9× in this paper
• Robustness — 8× in this paper · explained in 5 episodes
• Attention Mechanism — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Sparsity — 6× in this paper · explained in 4 episodes
• Guided attention — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Point Cloud Data — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation
• FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
• AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation

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