AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
CTM-Net is proposed, a systematic optimization scheme for PointPillars that aims to achieve efficient inference on high-performance computing platforms, providing a feasible basis for future migration to vehicle-mounted edge computing platforms.
This work introduces SAM-AD, a domain-specific pretraining strategy that fine-tunes SAM on autonomous-driving imagery to extract feature representations with rich semantic information, and develops the Depth-Guided Wavelet Attention (DGWA) module, which suppresses high-frequency sensor noise while preserving critical c...
Reliable 3D object detection is essential for autonomous-driving perception in complex traffic environments. Camera–LiDAR fusion combines dense visual semantics with precise point-cloud geometry to improve detection robustness. BEVFusion is a competitive bird’s-eye-view (BEV) fusion framework, but its high-capacity ima...
The rapid development of autonomous driving technology has increased the need for accurate vehicle environmental perception, with infrastructure 3D LiDAR playing a crucial role in enhancing traffic safety and management. However, processing its large-scale, sparse, and noisy point clouds remains challenging, compounded...
PH-PPC, a novel Point-Voxel based 3D object detection framework utilizing Height-Domain Attention and Point Cloud Pseudo-Completion, achieves competitive performance among voxel-based detectors and incorporates three key technical innovations to enhance robustness against occlusion.
PES-PointPillars is presented, an enhanced PointPillars detector with three coordinated design changes, with the complete model providing the strongest aggregate performance among the evaluated configurations.
Three-dimensional object detection is a key component of the perception module in autonomous driving systems. Compared to camera images, LiDAR point clouds provide richer spatial information, such as detailed structural and geometric cues of objects. However, existing 3D object detection methods face two major challeng...
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