RagNet3D: Learning distinguishable representation for pooled grids in 3D object detection
Authors:
- Jiaxin ChenJCJiaxin Chen
- Yuehui HanYHYuehui Han
- Zhiqiang YanZYZhiqiang Yan
- Jianjun QianJQJianjun Qian
- Jun LiJLJun Li
- Jian YangJYJian Yang
- 5 March 2025
- journal article
- Published by Elsevier in Neurocomputing
- Vol. 635
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (62176124, U24A20330, 62361166670)
This publication has 45 references indexed in Scilit:
- KPDet: Keypoint-based 3D object detection with Parametric Radius LearningNeurocomputing, 2023
- 3D Object Detection for Autonomous Driving: A Comprehensive SurveyInternational Journal of Computer Vision, 2023
- PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object DetectionInternational Journal of Computer Vision, 2022
- Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph2022
- Two-stage 3D object detection guided by position encodingNeurocomputing, 2022
- Spatial information enhancement network for 3D object detection from point cloudPattern Recognition, 2022
- ASCNet: 3D object detection from point cloud based on adaptive spatial context featuresNeurocomputing, 2022
- DVFENet: Dual-branch voxel feature extraction network for 3D object detectionNeurocomputing, 2021
- SSD: Single Shot MultiBox Detector2016
- Are we ready for autonomous driving? The KITTI vision benchmark suite2012