Abstract

We present Multi-task Learning with Localization Ambiguity Suppression for Occupancy Prediction (MiLO) as our

solution for camera-based 3D Occupancy Prediction Challenge at CVPR 2023. The proposed MiLO is unique in two

important aspects: (1) varying-depth multi-task learning to

incorporate perspective semantic prediction, depth estimation, and occupancy prediction for more robust representations; and (2) localization ambiguity suppression to adaptively suppress low-confident localization in camera-based

system with respect to object class and distance. In addition, our method employs several techniques to boost the

performance. Our final model achieves 52.45 points mIoU

without using external data and wins 2nd place in CVPR

2023 3D Occupancy Prediction Challenge.