Aug 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 40 references
TL;DR
Results validate the effectiveness of the proposed novel 3D object detection and tracking framework, termed ECF3DMOT, in advancing 3D object detection and tracking for autonomous driving.
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.
This work proposes a cascade optimization framework that systematically enhances feature representation and refines multimodal fusion, and introduces the Multi-Scale Contextual Fusion Module (MSCF) to reduce alignment bias.
Results indicate that the proposed enhanced detection algorithm built upon the PointPillars framework can effectively improve the detection accuracy and robustness of LiDAR-based 3D object detection in complex environments.
Yu Zhai, Sen Xie, Wen-Hao Li et al.· Electronics· 0 citations
This paper presents spatial-channel attention guided with Gumbel SUBSET SAMPLING and context fusion RCNN (SCAGCF-RCNN) as an incremental engineering improvement of the PV-RCNN two-stage point–voxel detection baseline, demonstrating competitive detection accuracy and robustness in challenging scenarios.
Hong-Xu Li, Shu-Yi Zhou, Yi-Tao Lu et al.· Engineering Research Express· 0 citations
Two lightweight and complementary modules to enhance voxel feature quality for NeRF-based 3D detection with consistent improvements over the NeRF-RPN baseline in both recall and precision are introduced.
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...
Zi-Ying Song, Lin Liu, Hong-Yu Pan et al.· 0 citations
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