Chenglu Wen
Xiamen University
126 Papers
231 Citations
Chenglu Wen is an academic researcher from Xiamen University. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 21, co-authored 90 publications. Previous affiliations of Chenglu Wen include Michigan State University & China Agricultural University.
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Papers
LO-Net: Deep Real-Time Lidar Odometry
Qing Li,Shaoyang Chen,Cheng Wang,Xin Li,Chenglu Wen,Ming Cheng,Jonathan Li +6 more
- 15 Jun 2019
TL;DR: Li et al. as discussed by the authors proposed a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation, which can effectively learn feature representation for LO estimation, and implicitly exploit the sequential dependencies and dynamics in the data.
3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association
TL;DR: A new 3D multi-object tracker to more robustly track objects that are temporarily missed by detectors, based on a novel data association scheme guided by prediction confidence, and it consists of a new predictor that employs a constant acceleration motion model to estimate future positions.
137
Local feature-based identification and classification for orchard insects
TL;DR: The best classification results under 10-fold cross-validation test were 4.57% and 5.95% using PCALC and SVM, indicating that the local region detector based insect classification method could be an effective way for insect identification and classification.
123
DeepSTD: Mining Spatio-Temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
TL;DR: A two-phase end-to-end deep learning framework, namely DeepSTD to uncover the spatio-temporal disturbances (STD) to predict the citywide traffic flow and demonstrates that DeepSTD outperforms the state-of-the-art methods.
116
Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data
TL;DR: Comparative studies with the existing traffic sign detection and recognition methods demonstrate that the proposed algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.
110