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 this https URL

该研究论文介绍了 OccFusion，一种直观高效的传感器融合框架，可用于预测三维占据情况。通过整合来自其他传感器（如激光雷达和环绕视图雷达）的特征，该框架提高了占据情况预测的准确性和稳健性，在 nuScenes 基准测试中取得了顶尖性能。