Multi-Echo LiDAR for 3D Object Detection
Author:
Affiliation:
1. Carnegie Mellon University
2. DENSO
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710421.pdf?arnumber=9710421
Reference34 articles.
1. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
2. 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-view Spatial Feature Fusion for 3D Object Detection
3. STD: Sparse-to-Dense 3D Object Detector for Point Cloud
4. 3DSSD: Point-Based 3D Single Stage Object Detector
5. Cross-modality 3d object detection;zhu;arXiv preprint arXiv 2008 10856,2020
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1. DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01
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3. BEV-Guided Multi-Modality Fusion for Driving Perception;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
4. LiDAR-in-the-Loop Hyperparameter Optimization;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06
5. Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System;Remote Sensing;2023-01-20
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