  Bounded by the inherent ambiguity of depth perception, contemporary
multi-view 3D object detection methods fall into the performance bottleneck.
Intuitively, leveraging temporal multi-view stereo (MVS) technology is the
natural knowledge for tackling this ambiguity. However, traditional attempts of
MVS has two limitations when applying to 3D object detection scenes: 1) The
affinity measurement among all views suffers expensive computational cost; 2)
It is difficult to deal with outdoor scenarios where objects are often mobile.
To this end, we propose BEVStereo++: by introducing a dynamic temporal stereo
strategy, BEVStereo++ is able to cut down the harm that is brought by
introducing temporal stereo when dealing with those two scenarios. Going one
step further, we apply Motion Compensation Module and long sequence Frame
Fusion to BEVStereo++, which shows further performance boosting and error
reduction. Without bells and whistles, BEVStereo++ achieves
state-of-the-art(SOTA) on both Waymo and nuScenes dataset.
