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We introduce a framework for multi-camera 3D object detection. In contrast toexisting works, which estimate 3D bounding boxes directly from monocular imagesor use depth prediction networks to generate input for 3D object detection from2D information, our method manipulates predictions directly in 3D space. Ourarchitecture extracts 2D features from multiple camera images and then uses asparse set of 3D object queries to index into these 2D features, linking 3Dpositions to multi-view images using camera transformation matrices. Finally,our model makes a bounding box prediction per object query, using a set-to-setloss to measure the discrepancy between the ground-truth and the prediction.This top-down approach outperforms its bottom-up counterpart in which objectbounding box prediction follows per-pixel depth estimation, since it does notsuffer from the compounding error introduced by a depth prediction model.Moreover, our method does not require post-processing such as non-maximumsuppression, dramatically improving inference speed. We achievestate-of-the-art performance on the nuScenes autonomous driving benchmark.

DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries