  In LiDAR-based 3D detection, history point clouds contain rich temporal
information helpful for future prediction. In the same way, history detections
should contribute to future detections. In this paper, we propose a detection
enhancement method, namely FrameFusion, which improves 3D object detection
results by fusing history frames. In FrameFusion, we ''forward'' history frames
to the current frame and apply weighted Non-Maximum-Suppression on dense
bounding boxes to obtain a fused frame with merged boxes. To ''forward''
frames, we use vehicle motion models to estimate the future pose of the
bounding boxes. However, the commonly used constant velocity model fails
naturally on turning vehicles, so we explore two vehicle motion models to
address this issue. On Waymo Open Dataset, our FrameFusion method consistently
improves the performance of various 3D detectors by about $2$ vehicle level 2
APH with negligible latency and slightly enhances the performance of the
temporal fusion method MPPNet. We also conduct extensive experiments on motion
model selection.
