Core Concepts
Bird's-eye-view detection paradigm improves unified monocular 3D object detection.
Abstract
UniMODE introduces innovative techniques like uneven BEV grid design, sparse BEV feature projection, and unified domain alignment to address challenges in unified 3D object detection. The proposal head stabilizes convergence, while the two-stage architecture enhances accuracy. UniMODE outperforms Cube RCNN by 4.9% on the Omni3D dataset. DALN and class alignment loss handle heterogeneous domains effectively.
Stats
UniMODE surpasses Cube RCNN by 4.9% on the Omni3D dataset.
Sparse BEV feature projection reduces computational cost by 82.6%.
UniMODE achieves state-of-the-art performance on various sub-datasets in Omni3D.