AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A spatial-channel collaborative modeling framework named SPMixNet is introduced to improve fine-grained 3D scene understanding and effectively mitigates the structural and representational limitations of existing projection-based methods, providing a promising solution for accurate segmentation of small and structurally complex objects in large-scale 3D scenes.
Point cloud registration is a fundamental task in 3D vision, however, large-scale outdoor LiDAR point clouds, characterized by their immense size and structural complexity, present significant challenges in highly dynamic environments. Existing methods often employ semantic segmentation as preprocessing, offering the p...
This paper replaces the Stem layer in FRNet with the proposed FD-Stem, which improves feature representation while reducing computational complexity, and introduces long-range modeling capability with limited additional parameters, enabling effective learning of both spatial and channel-wise representations.
The proposed PDES-Net enhances segmentation performance while maintaining advantages in model parameters and inference speed, and achieves a well-balanced trade-off between accuracy and computational efficiency.
Source-Free Domain Adaptation (SFDA) offers a practical solution for deploying LiDAR point cloud segmentation models in new environments where source data cannot be accessed due to privacy constraints and storage limitations. Existing SFDA methods, however, are primarily designed for images and fail to account for the...
Semantic segmentation of large-scale power-corridor LiDAR point clouds is essential for remote sensing-based transmission line inspection, vegetation encroachment monitoring, and intelligent grid maintenance. However, existing methods still struggle with massive data volumes, severe class imbalance, sparse power-relate...
Accurate semantic segmentation of large-scale outdoor LiDAR point clouds remains a challenging endeavor, primarily due to ambiguous class transitions at object interfaces, non-uniform sampling density across the surveyed area, and shared geometric signatures among distinct object categories. This paper proposes GFE-Net...
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