ArticleScientific reports2025
PillarFocusNet for 3D object detection with perceptual diffusion and key feature understanding.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multimodal Sensor Fusion in Autonomous Vehicles: Technologies, Architectures, and Open Challenges.Sensors (Basel, Switzerland) · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
This study presents PillarFocusNet, a novel network about 3D point cloud object detection that optimizes the PointPillars framework to improve detection performance. First, we propose the Pillar Clustering Sampling Method to address the sparse and uneven distribution of 3D point cloud data. Second, we introduce the Mixed Pooling Dilated Convolution Layer (MPDC layer) to enhance feature extraction. Finally, we integrate the Space-Channel Synergistic Enhancement Module (SCS-EM) to improve feature representation across both spatial and channel dimensions. Results from KITTI dataset experiments demonstrate that PillarFocusNet enhances bbox, bev, and 3d detection performance by 1.3%, 2.9%, and 3.4%, respectively, when compared to PointPillars. The code and models are publicly available at https://github.com/Gaoooyhan/PillarFocusNet .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.