TL;DR
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.
High-precision perception is fundamental to safe autonomous driving, and BEV-based 3D object detection via lidar-camera fusion plays a crucial role in improving detection accuracy and robustness. To address insufficient feature representation, spatial misalignment, and the limitations of static fusion strategies, this...
A aggregated Euclidean distance weighted box fusion method, which aggregates complementary information from multiple candidate boxes during post-processing to improve bounding-box selection and localization accuracy, and a hybrid deformable half-conv (HDHC) module that jointly enhances global and local feature represen...
In the realm of autonomous driving, 3D object detection based on LiDAR point clouds has emerged as a pivotal technology. To enhance the accuracy and efficiency of 3D object detection, this paper introduces Spatial-Channel Attention Guided with Gumbel Subset Sampling and Context Fusion RCNN (SCAGCF-RCNN), a two-stage...
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.
Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and camera sensors. However, existing methods often fail to maintain robustness under out-of-distribution (OOD) corruptions caused by sensor noise, adverse weather, and env...
To address the challenges of insufficient feature representation and the difficulty of detecting sparse and distant objects in UAV-borne LiDAR point clouds—which exhibit significantly lower point density than terrestrial/mobile LiDAR scans—this paper proposes an enhanced detection algorithm built upon the PointPillars...
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