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Found 4 papers, 1 papers with code
MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data
In this work, we propose \textit{MVFuseNet}, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data.
Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
Our model builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data as well as rasterized high-definition map to perform detection and prediction tasks.
LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.
Training Complex Models with Multi-Task Weak Supervision
Snorkel MeTaL: A framework for training models with multi-task weak supervision
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