Self-Attentive Edge-Convolution Bipartite Graph Network for 3D Object Reconstruction from Point Clouds
IEEE Transactions on Multimedia
Michigan State University
Smart Sensing Lab
Computer vision and machine learning applied to plant and animal agriculture, human medicine, and autonomous vehicles.
3,800+
Citations
30+
Publications
Faculty Director
Daniel Morris, Ph.D.
Associate Professor
Biosystems & Agricultural Engineering
Electrical & Computer Engineering
Sensing Intelligence in Agriculture and Autonomous Vehicles
We develop algorithms that enable machines to perceive, understand, and act in complex environments across agriculture and transportation.
01 / Smart Agriculture
Plant & Animal Intelligence
Individual animal monitoring, automated crop phenotyping, swine posture recognition, dense-leaves segmentation, and label-efficient learning for precision agriculture.
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02 / Autonomous Vehicles
Multi-Modal Sensor Fusion
Radar-camera fusion, LiDAR-camera object detection (CLOCs), depth completion, multi-object tracking, HD map localization, and autonomous racing.
View Projects →Publications
Latest Publications
Recent peer-reviewed work spanning top computer vision venues including CVPR, ICCV, IROS, and WACV.
An Automatic Scoring Method for Swine Leg Structure Based on 3D Point Clouds
Agriculture 16, no. 6: 706
RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection
CVPR 2025
Self-Annotated 3D Geometric Learning for Smeared Points Removal
WACV 2024