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Autoware Safe Autonomy Seminar Autonomous vehicles rely on Efficient 3D Object Detection via Point-Pillar Feature Fusion for Autonomous Driving The point cloud data is first projected on bird's eye view format and applied the This is a presentation for an IEEE ICETCI 2021 paper Analysis of voxel-based Paper: Vision Transformers (ViTs) have achieved state-of-the-art results on various computer ... This video provides a short overview of our recent paper "Vote3Deep: Fast
Video presentation for the IROS 2021 paper. ArXiV preprint: code release:Â ... This is a demo of running our PVCNN on NVIDIA Jetson devices (for
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Lidar based 3D Object Detection using PV-RCNN
Efficient 3D Perception for Autonomous Vehicles (Zhijian Liu @ CVPR 2023 ECV)
Efficient 3D Object Detection using Multiple Pose-specific Classifiers
Efficient 3D Object Detection via Point-Pillar Feature Fusion for Autonomous Driving
Real Time 3D Object Detection only using Point Cloud data
ICETCI Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems
[ML4AD2023] Explainable Multi-Camera 3D Object Detection with Transformer-Based Saliency Maps
EfficientDet: Scalable and Efficient Object Detection
Efficient 3D Perception for Autonomous Vehicles (Zhijian Liu)
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
MambaDet: Efficient 3D Object Detection with Mamba
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
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Last Updated: June 14, 2026
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