OccFusion: A Straightforward and Effective Multi-Sensor Fusion Framework for 3D Occupancy Prediction
arxiv(2024)
摘要
This paper introduces OccFusion, a straightforward and efficient sensor
fusion framework for predicting 3D occupancy. A comprehensive understanding of
3D scenes is crucial in autonomous driving, and recent models for 3D semantic
occupancy prediction have successfully addressed the challenge of describing
real-world objects with varied shapes and classes. However, existing methods
for 3D occupancy prediction heavily rely on surround-view camera images, making
them susceptible to changes in lighting and weather conditions. By integrating
features from additional sensors, such as lidar and surround view radars, our
framework enhances the accuracy and robustness of occupancy prediction,
resulting in top-tier performance on the nuScenes benchmark. Furthermore,
extensive experiments conducted on the nuScenes dataset, including challenging
night and rainy scenarios, confirm the superior performance of our sensor
fusion strategy across various perception ranges. The code for this framework
will be made available at https://github.com/DanielMing123/OCCFusion.
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