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A key contributor to recent progress in 3D detection from single images ismonocular depth estimation. Existing methods focus on how to leverage depthexplicitly, by generating pseudo-pointclouds or providing attention cues forimage features. More recent works leverage depth prediction as a pretrainingtask and fine-tune the depth representation while training it for 3D detection.However, the adaptation is insufficient and is limited in scale by manuallabels. In this work, we propose to further align depth representation with thetarget domain in unsupervised fashions. Our methods leverage commonly availableLiDAR or RGB videos during training time to fine-tune the depth representation,which leads to improved 3D detectors. Especially when using RGB videos, we showthat our two-stage training by first generating pseudo-depth labels is criticalbecause of the inconsistency in loss distribution between the two tasks. Witheither type of reference data, our multi-task learning approach improves overthe state of the art on both KITTI and NuScenes, while matching the test-timecomplexity of its single task sub-network.

Depth Is All You Need for Monocular 3D Detection