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Transfomer-based approaches advance the recent development of multi-camera 3Ddetection both in academia and industry. In a vanilla transformer architecture,queries are randomly initialised and optimised for the whole dataset, withoutconsidering the differences among input frames. In this work, we propose toleverage the predictions from an image backbone, which is often highlyoptimised for 2D tasks, as priors to the transformer part of a 3D detectionnetwork. The method works by (1). augmenting image feature maps with 2D priors,(2). sampling query locations via ray-casting along 2D box centroids, as wellas (3). initialising query features with object-level image features.Experimental results shows that 2D priors not only help the model convergefaster, but also largely improve the baseline approach by up to 12% in terms ofaverage precision.

Priors are Powerful: Improving a Transformer for Multi-camera 3D Detection with 2D Priors