Recent camera-based 3D object detection methods have introduced sequential
frames to improve the detection performance hoping that multiple frames would
mitigate the large depth estimation error. Despite improved detection
performance, prior works rely on naive fusion methods (e.g., concatenation) or
are limited to static scenes (e.g., temporal stereo), neglecting the importance
of the motion cue of objects. These approaches do not fully exploit the
potential of sequential images and show limited performance improvements. To
address this limitation, we propose a novel 3D object detection model, P2D
(Predict to Detect), that integrates a prediction scheme into a detection
framework to explicitly extract and leverage motion features. P2D predicts
object information in the current frame using solely past frames to learn
temporal motion features. We then introduce a novel temporal feature
aggregation method that attentively exploits Bird's-Eye-View (BEV) features
based on predicted object information, resulting in accurate 3D object
detection. Experimental results demonstrate that P2D improves mAP and NDS by
3.0% and 3.7% compared to the sequential image-based baseline, illustrating
that incorporating a prediction scheme can significantly improve detection
accuracy.

本文提出了一种名为 P2D 的新型三维物体检测模型，它将预测方案整合到检测框架中，显式提取和利用运动特征，以及引入基于预测物体信息的 Bird's-Eye-View（BEV）特征的新型时间特征聚合方法，结果表明，与基线相比，P2D 可以显著提高检测精度。