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Transformer-based methods have swept the benchmarks on 2D and 3D detection onimages. Because tokenization before the attention mechanism drops the spatialinformation, positional encoding becomes critical for those methods. Recentworks found that encodings based on samples of the 3D viewing rays cansignificantly improve the quality of multi-camera 3D object detection. Wehypothesize that 3D point locations can provide more information than rays.Therefore, we introduce 3D point positional encoding, 3DPPE, to the 3Ddetection Transformer decoder. Although 3D measurements are not available atthe inference time of monocular 3D object detection, 3DPPE uses predicted depthto approximate the real point positions. Our hybriddepth module combines directand categorical depth to estimate the refined depth of each pixel. Despite theapproximation, 3DPPE achieves 46.0 mAP and 51.4 NDS on the competitive nuScenesdataset, significantly outperforming encodings based on ray samples. We makethe codes available at https://github.com/drilistbox/3DPPE.

3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers